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Faggioli G, Menotti L, Marchesin S, Chió A, Dagliati A, de Carvalho M, Gromicho M, Manera U, Tavazzi E, Di Nunzio GM, Silvello G, Ferro N. An extensible and unifying approach to retrospective clinical data modeling: the BrainTeaser Ontology. J Biomed Semantics 2024; 15:16. [PMID: 39210467 PMCID: PMC11363415 DOI: 10.1186/s13326-024-00317-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2024] [Accepted: 07/29/2024] [Indexed: 09/04/2024] Open
Abstract
Automatic disease progression prediction models require large amounts of training data, which are seldom available, especially when it comes to rare diseases. A possible solution is to integrate data from different medical centres. Nevertheless, various centres often follow diverse data collection procedures and assign different semantics to collected data. Ontologies, used as schemas for interoperable knowledge bases, represent a state-of-the-art solution to homologate the semantics and foster data integration from various sources. This work presents the BrainTeaser Ontology (BTO), an ontology that models the clinical data associated with two brain-related rare diseases (ALS and MS) in a comprehensive and modular manner. BTO assists in organizing and standardizing the data collected during patient follow-up. It was created by harmonizing schemas currently used by multiple medical centers into a common ontology, following a bottom-up approach. As a result, BTO effectively addresses the practical data collection needs of various real-world situations and promotes data portability and interoperability. BTO captures various clinical occurrences, such as disease onset, symptoms, diagnostic and therapeutic procedures, and relapses, using an event-based approach. Developed in collaboration with medical partners and domain experts, BTO offers a holistic view of ALS and MS for supporting the representation of retrospective and prospective data. Furthermore, BTO adheres to Open Science and FAIR (Findable, Accessible, Interoperable, and Reusable) principles, making it a reliable framework for developing predictive tools to aid in medical decision-making and patient care. Although BTO is designed for ALS and MS, its modular structure makes it easily extendable to other brain-related diseases, showcasing its potential for broader applicability.Database URL https://zenodo.org/records/7886998 .
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Affiliation(s)
- Guglielmo Faggioli
- Department of Information Engineering, University of Padova, Padova, Italy.
| | - Laura Menotti
- Department of Information Engineering, University of Padova, Padova, Italy.
| | - Stefano Marchesin
- Department of Information Engineering, University of Padova, Padova, Italy.
| | - Adriano Chió
- Rita Levi Montalcini Department of Neuroscience, University of Turin, Turin, Italy
- Institute of Cognitive Sciences and Technologies, C.N.R, Rome, Italy
- Azienda Ospedaliero Universitaria Cittá della Salute e della Scienza, Turin, Italy
| | - Arianna Dagliati
- Department of Industrial and Information Engineering, University of Pavia, Pavia, Italy
| | - Mamede de Carvalho
- Faculdade de Medicina, Instituto de Medicina Molecular, Universidade de Lisboa, Lisbon, Portugal
| | - Marta Gromicho
- Faculdade de Medicina, Instituto de Medicina Molecular, Universidade de Lisboa, Lisbon, Portugal
| | - Umberto Manera
- Rita Levi Montalcini Department of Neuroscience, University of Turin, Turin, Italy
| | | | | | - Gianmaria Silvello
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Nicola Ferro
- Department of Information Engineering, University of Padova, Padova, Italy
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Romao P, Neuenschwander S, Zbinden C, Seidel K, Sariyar M. An ontology-based tool for modeling and documenting events in neurosurgery. BMC Med Inform Decis Mak 2024; 24:216. [PMID: 39085883 PMCID: PMC11293115 DOI: 10.1186/s12911-024-02615-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 07/17/2024] [Indexed: 08/02/2024] Open
Abstract
BACKGROUND Intraoperative neurophysiological monitoring (IOM) plays a pivotal role in enhancing patient safety during neurosurgical procedures. This vital technique involves the continuous measurement of evoked potentials to provide early warnings and ensure the preservation of critical neural structures. One of the primary challenges has been the effective documentation of IOM events with semantically enriched characterizations. This study aimed to address this challenge by developing an ontology-based tool. METHODS We structured the development of the IOM Documentation Ontology (IOMDO) and the associated tool into three distinct phases. The initial phase focused on the ontology's creation, drawing from the OBO (Open Biological and Biomedical Ontology) principles. The subsequent phase involved agile software development, a flexible approach to encapsulate the diverse requirements and swiftly produce a prototype. The last phase entailed practical evaluation within real-world documentation settings. This crucial stage enabled us to gather firsthand insights, assessing the tool's functionality and efficacy. The observations made during this phase formed the basis for essential adjustments to ensure the tool's productive utilization. RESULTS The core entities of the ontology revolve around central aspects of IOM, including measurements characterized by timestamp, type, values, and location. Concepts and terms of several ontologies were integrated into IOMDO, e.g., the Foundation Model of Anatomy (FMA), the Human Phenotype Ontology (HPO) and the ontology for surgical process models (OntoSPM) related to general surgical terms. The software tool developed for extending the ontology and the associated knowledge base was built with JavaFX for the user-friendly frontend and Apache Jena for the robust backend. The tool's evaluation involved test users who unanimously found the interface accessible and usable, even for those without extensive technical expertise. CONCLUSIONS Through the establishment of a structured and standardized framework for characterizing IOM events, our ontology-based tool holds the potential to enhance the quality of documentation, benefiting patient care by improving the foundation for informed decision-making. Furthermore, researchers can leverage the semantically enriched data to identify trends, patterns, and areas for surgical practice enhancement. To optimize documentation through ontology-based approaches, it's crucial to address potential modeling issues that are associated with the Ontology of Adverse Events.
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Affiliation(s)
| | | | - Chantal Zbinden
- Department of Neurosurgery, Inselspital, University Hospital, Bern, Switzerland
| | - Kathleen Seidel
- Department of Neurosurgery, Inselspital, University Hospital, Bern, Switzerland
| | - Murat Sariyar
- Bern University of Applied Sciences, Bern, Switzerland.
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Faria D, Eugénio P, Contreiras Silva M, Balbi L, Bedran G, Kallor AA, Nunes S, Palkowski A, Waleron M, Alfaro JA, Pesquita C. The Immunopeptidomics Ontology (ImPO). Database (Oxford) 2024; 2024:baae014. [PMID: 38857186 PMCID: PMC11164101 DOI: 10.1093/database/baae014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 11/30/2023] [Accepted: 02/22/2024] [Indexed: 06/12/2024]
Abstract
The adaptive immune response plays a vital role in eliminating infected and aberrant cells from the body. This process hinges on the presentation of short peptides by major histocompatibility complex Class I molecules on the cell surface. Immunopeptidomics, the study of peptides displayed on cells, delves into the wide variety of these peptides. Understanding the mechanisms behind antigen processing and presentation is crucial for effectively evaluating cancer immunotherapies. As an emerging domain, immunopeptidomics currently lacks standardization-there is neither an established terminology nor formally defined semantics-a critical concern considering the complexity, heterogeneity, and growing volume of data involved in immunopeptidomics studies. Additionally, there is a disconnection between how the proteomics community delivers the information about antigen presentation and its uptake by the clinical genomics community. Considering the significant relevance of immunopeptidomics in cancer, this shortcoming must be addressed to bridge the gap between research and clinical practice. In this work, we detail the development of the ImmunoPeptidomics Ontology, ImPO, the first effort at standardizing the terminology and semantics in the domain. ImPO aims to encapsulate and systematize data generated by immunopeptidomics experimental processes and bioinformatics analysis. ImPO establishes cross-references to 24 relevant ontologies, including the National Cancer Institute Thesaurus, Mondo Disease Ontology, Logical Observation Identifier Names and Codes and Experimental Factor Ontology. Although ImPO was developed using expert knowledge to characterize a large and representative data collection, it may be readily used to encode other datasets within the domain. Ultimately, ImPO facilitates data integration and analysis, enabling querying, inference and knowledge generation and importantly bridging the gap between the clinical proteomics and genomics communities. As the field of immunogenomics uses protein-level immunopeptidomics data, we expect ImPO to play a key role in supporting a rich and standardized description of the large-scale data that emerging high-throughput technologies are expected to bring in the near future. Ontology URL: https://zenodo.org/record/10237571 Project GitHub: https://github.com/liseda-lab/ImPO/blob/main/ImPO.owl.
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Affiliation(s)
- Daniel Faria
- INESC-ID, Instituto Superior Técnico, Universidade de Lisboa, Rua Alves Redol, 9, Lisboa 1000-029, Portugal
| | - Patrícia Eugénio
- LASIGE, Faculdade de Ciências da Universidade de Lisboa, Campo Grande, Lisboa 1749-016, Portugal
| | - Marta Contreiras Silva
- LASIGE, Faculdade de Ciências da Universidade de Lisboa, Campo Grande, Lisboa 1749-016, Portugal
| | - Laura Balbi
- LASIGE, Faculdade de Ciências da Universidade de Lisboa, Campo Grande, Lisboa 1749-016, Portugal
| | - Georges Bedran
- International Centre for Cancer Vaccine Science, University of Gdansk, ul. Kładki 24, Gdańsk 80-822, Poland
| | - Ashwin Adrian Kallor
- International Centre for Cancer Vaccine Science, University of Gdansk, ul. Kładki 24, Gdańsk 80-822, Poland
| | - Susana Nunes
- LASIGE, Faculdade de Ciências da Universidade de Lisboa, Campo Grande, Lisboa 1749-016, Portugal
| | - Aleksander Palkowski
- International Centre for Cancer Vaccine Science, University of Gdansk, ul. Kładki 24, Gdańsk 80-822, Poland
| | - Michal Waleron
- International Centre for Cancer Vaccine Science, University of Gdansk, ul. Kładki 24, Gdańsk 80-822, Poland
| | - Javier A Alfaro
- International Centre for Cancer Vaccine Science, University of Gdansk, ul. Kładki 24, Gdańsk 80-822, Poland
- Department of Biochemistry and Microbiology, University of Victoria, 3800 Finnerty Rd, Victoria, British Columbia, BC V8P 5C2, Canada
- Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh, Old College, South Bridge, Edinburgh, EH8 9YL, UK
- The Canadian Association for Responsible AI in Medicine, Victoria, Canada
| | - Catia Pesquita
- LASIGE, Faculdade de Ciências da Universidade de Lisboa, Campo Grande, Lisboa 1749-016, Portugal
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Soliman Y, Yakandawala U, Leong C, Garlock ES, Brinkman FSL, Winsor GL, Kozyrskyj AL, Mandhane PJ, Turvey SE, Moraes TJ, Subbarao P, Nickel NC, Thiessen K, Azad MB, Kelly LE. The use of prescription medications and non-prescription medications during lactation in a prospective Canadian cohort study. Int Breastfeed J 2024; 19:23. [PMID: 38589955 PMCID: PMC11000278 DOI: 10.1186/s13006-024-00628-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Accepted: 03/17/2024] [Indexed: 04/10/2024] Open
Abstract
BACKGROUND A lack of safety data on postpartum medication use presents a potential barrier to breastfeeding and may result in infant exposure to medications in breastmilk. The type and extent of medication use by lactating women requires investigation. METHODS Data were collected from the CHILD Cohort Study which enrolled pregnant women across Canada between 2008 and 2012. Participants completed questionnaires regarding medications and non-prescription medications used and breastfeeding status at 3, 6 and 12 months postpartum. Medications, along with self-reported reasons for medication use, were categorized by ontologies [hierarchical controlled vocabulary] as part of a large-scale curation effort to enable more robust investigations of reasons for medication use. RESULTS A total of 3542 mother-infant dyads were recruited to the CHILD study. Breastfeeding rates were 87.4%, 75.3%, 45.5% at 3, 6 and 12 months respectively. About 40% of women who were breastfeeding at 3 months used at least one prescription medication during the first three months postpartum; this proportion decreased over time to 29.5% % at 6 months and 32.8% at 12 months. The most commonly used prescription medication by breastfeeding women was domperidone at 3 months (9.0%, n = 229/2540) and 6 months (5.6%, n = 109/1948), and norethisterone at 12 months (4.1%, n = 48/1180). The vast majority of domperidone use by breastfeeding women (97.3%) was for lactation purposes which is off-label (signifying unapproved use of an approved medication). Non-prescription medications were more often used among breastfeeding than non-breastfeeding women (67.6% versus 48.9% at 3 months, p < 0.0001), The most commonly used non-prescription medications were multivitamins and Vitamin D at 3, 6 and 12 months postpartum. CONCLUSIONS In Canada, medication use is common postpartum; 40% of breastfeeding women use prescription medications in the first 3 months postpartum. A diverse range of medications were used, with many women taking more than one prescription and non-prescription medicines. The most commonly used prescription medication by breastfeeding women were domperidone for off-label lactation support, signalling a need for more data on the efficacy of domperidone for this indication. This data should inform research priorities and communication strategies developed to optimize care during lactation.
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Affiliation(s)
- Youstina Soliman
- Department of Pediatrics and Child Health, University of Manitoba, Winnipeg, MB, Canada
| | - Uma Yakandawala
- George and Fay Yee Centre for Healthcare Innovation, Winnipeg, MB, Canada
- College of Pharmacy, Rady Faculty of Health Science, University of Manitoba, Winnipeg, MB, Canada
| | - Christine Leong
- College of Pharmacy, Rady Faculty of Health Science, University of Manitoba, Winnipeg, MB, Canada
| | - Emma S Garlock
- Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, Canada
| | - Fiona S L Brinkman
- Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, Canada
| | - Geoffrey L Winsor
- Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, Canada
| | - Anita L Kozyrskyj
- Department of Pediatrics, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, AB, Canada
| | - Piushkumar J Mandhane
- Department of Pediatrics, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, AB, Canada
| | - Stuart E Turvey
- Department of Pediatrics, University of British Columbia, Vancouver, Canada
| | - Theo J Moraes
- Department of Pediatrics, Hospital for Sick Children, University of Toronto, Toronto, Canada
| | - Padmaja Subbarao
- Department of Pediatrics, Hospital for Sick Children, University of Toronto, Toronto, Canada
| | - Nathan C Nickel
- Department of Community Health Sciences, University of Manitoba, Winnipeg, MB, Canada
- Manitoba Interdisciplinary Lactation Centre (MILC), Winnipeg, MB, Canada
| | - Kellie Thiessen
- College of Nursing, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
| | - Meghan B Azad
- Department of Pediatrics and Child Health, University of Manitoba, Winnipeg, MB, Canada
- Manitoba Interdisciplinary Lactation Centre (MILC), Children's Hospital Research Institute of Manitoba, Winnipeg, MB, Canada
| | - Lauren E Kelly
- Department of Pediatrics and Child Health, University of Manitoba, Winnipeg, MB, Canada.
- George and Fay Yee Centre for Healthcare Innovation, Winnipeg, MB, Canada.
- Manitoba Interdisciplinary Lactation Centre (MILC), Children's Hospital Research Institute of Manitoba, Winnipeg, MB, Canada.
- , 417-753 McDermot Ave, R3E 0T6, Winnipeg, MB, Canada.
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Caballero-Oteyza A, Crisponi L, Peng XP, Yauy K, Volpi S, Giardino S, Freeman AF, Grimbacher B, Proietti M. GenIA, the Genetic Immunology Advisor database for inborn errors of immunity. J Allergy Clin Immunol 2024; 153:831-843. [PMID: 38040041 DOI: 10.1016/j.jaci.2023.11.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 10/23/2023] [Accepted: 11/15/2023] [Indexed: 12/03/2023]
Abstract
BACKGROUND To date, no publicly accessible platform has captured and synthesized all of the layered dimensions of genotypic, phenotypic, and mechanistic information published in the field of inborn errors of immunity (IEIs). Such a platform would represent the extensive and complex landscape of IEIs and could increase the rate of diagnosis in patients with a suspected IEI, which remains unacceptably low. OBJECTIVE Our aim was to create an expertly curated, patient-centered, multidimensional IEI database that enables aggregation and sophisticated data interrogation and promotes involvement from diverse stakeholders across the community. METHODS The database structure was designed following a subject-centered model and written in Structured Query Language (SQL). The web application is written in Hypertext Preprocessor (PHP), Hypertext Markup Language (HTML), Cascading Style Sheets (CSS), and JavaScript. All data stored in the Genetic Immunology Advisor (GenIA) are extracted by manually reviewing published research articles. RESULTS We completed data collection and curation for 24 pilot genes. Using these data, we have exemplified how GenIA can provide quick access to structured, longitudinal, more thorough, comprehensive, and up-to-date IEI knowledge than do currently existing databases, such as ClinGen, Human Phenotype Ontology (HPO), ClinVar, or Online Mendelian Inheritance in Man (OMIM), with which GenIA intends to dovetail. CONCLUSIONS GenIA strives to accurately capture the extensive genetic, mechanistic, and phenotypic heterogeneity found across IEIs, as well as genetic paradigms and diagnostic pitfalls associated with individual genes and conditions. The IEI community's involvement will help promote GenIA as an enduring resource that supports and improves knowledge sharing, research, diagnosis, and care for patients with genetic immune disease.
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Affiliation(s)
- Andrés Caballero-Oteyza
- Clinic for Immunology and Rheumatology, Hanover Medical School, Hanover, Germany; RESiST-Cluster of Excellence 2155, Hanover Medical School, Hanover, Germany; Institute for Immunodeficiency, Center for Chronic Immunodeficiency, University Hospital Freiburg, Freiburg, Germany.
| | - Laura Crisponi
- Institute for Genetic and Biomedical Research, The National Research Council, Monserrato, Cagliari, Italy
| | - Xiao P Peng
- Department of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, Md
| | - Kevin Yauy
- University of Montpellier, LIRMM, CNRS, Reference Center for Congenital Anomalies, Clinical Genetic Unit, Montpellier University Hospital Center, Montpellier, France
| | - Stefano Volpi
- Center for Autoinflammatory Diseases and Immunodeficiencies, Pediatric Rheumatology Clinic, IRCCS Istituto Giannina Gaslini, Genova, and DINOGMI, Università degli Studi di Genova, Genova, Italy
| | - Stefano Giardino
- Hematopoietic Stem Cell Transplantation Unit, IRCCS Istituto Giannina Gaslini, Genova, Italy
| | - Alexandra F Freeman
- Laboratory of Clinical Immunology and Microbiology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Md
| | - Bodo Grimbacher
- Institute for Immunodeficiency, Center for Chronic Immunodeficiency, University Hospital Freiburg, Freiburg, Germany; Clinic of Rheumatology and Clinical Immunology, Center for Chronic Immunodeficiency, Medical Center, Faculty of Medicine, Albert-Ludwigs University of Freiburg, Freiburg, Germany; RESiST-Cluster of Excellence 2155, Hanover Medical School, Satellite Center Freiburg, Freiburg, Germany; German Center for Infection Research, Satellite Center Freiburg, Freiburg, Germany; Centre for Integrative Biological Signalling Studies, Albert-Ludwigs University of Freiburg, Freiburg, Germany
| | - Michele Proietti
- Clinic for Immunology and Rheumatology, Hanover Medical School, Hanover, Germany; RESiST-Cluster of Excellence 2155, Hanover Medical School, Hanover, Germany; Institute for Immunodeficiency, Center for Chronic Immunodeficiency, University Hospital Freiburg, Freiburg, Germany.
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Sankaranarayanapillai M, Wang S, Ji H, Song HY, Tao C. Lessons learned from annotation of VAERS reports on adverse events following influenza vaccination and related to Guillain-Barré syndrome. BMC Med Inform Decis Mak 2024; 23:298. [PMID: 38183034 PMCID: PMC10770878 DOI: 10.1186/s12911-023-02374-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Accepted: 11/14/2023] [Indexed: 01/07/2024] Open
Abstract
BACKGROUND Vaccine Adverse Events ReportingSystem (VAERS) is a promising resource of tracking adverse events following immunization. Medical Dictionary for Regulatory Activities (MedDRA) terminology used for coding adverse events in VAERS reports has several limitations. We focus on developing an automated system for semantic extraction of adverse events following vaccination and their temporal relationships for a better understanding of VAERS data and its integration into other applications. The aim of the present studyis to summarize the lessons learned during the initial phase of this project in annotating adverse events following influenza vaccination and related to Guillain-Barré syndrome (GBS). We emphasize on identifying the limitations of VAERS and MedDRA. RESULTS We collected 282 VAERS reports documented between 1990 and 2016 and shortlisted those with at least 1,100 characters in the report. We used a subset of 50 reports for the preliminary investigation and annotated all adverse events following influenza vaccination by mapping to representative MedDRA terms. Associated time expressions were annotated when available. We used 16 System Organ Class (SOC) level MedDRA terms to map GBS related adverse events and expanded some SOC terms to Lowest Level Terms (LLT) for granular representation. We annotated three broad categories of events such as problems, clinical investigations, and treatments/procedures. The inter-annotator agreement of events achieved was 86%. Incomplete reports, typographical errors, lack of clarity and coherence, repeated texts, unavailability of associated temporal information, difficulty to interpret due to incorrect grammar, use of generalized terms to describe adverse events / symptoms, uncommon abbreviations, difficulty annotating multiple events with a conjunction / common phrase, irrelevant historical events and coexisting events were some of the challenges encountered. Some of the limitations we noted are in agreement with previous reports. CONCLUSIONS We reported the challenges encountered and lessons learned during annotation of adverse events in VAERS reports following influenza vaccination and related to GBS. Though the challenges may be due to the inevitable limitations of public reporting systems and widely reported limitations of MedDRA, we emphasize the need to understand these limitations and extraction of other supportive information for a better understanding of adverse events following vaccination.
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Affiliation(s)
| | - Su Wang
- McWilliams School of Biomedical Informatics, The University of Texas Health Science Center, Houston, TX, USA
| | - Hangyu Ji
- McWilliams School of Biomedical Informatics, The University of Texas Health Science Center, Houston, TX, USA
| | - Hsing-Yi Song
- McWilliams School of Biomedical Informatics, The University of Texas Health Science Center, Houston, TX, USA
| | - Cui Tao
- McWilliams School of Biomedical Informatics, The University of Texas Health Science Center, Houston, TX, USA.
- Department of AI and Informatics, Mayo Clinic, Jacksonville, FL, USA.
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Taheri Moghadam S, Sheikhtaheri A, Hooman N. Patient safety classifications, taxonomies and ontologies, part 2: A systematic review on content coverage. J Biomed Inform 2023; 148:104549. [PMID: 37984548 DOI: 10.1016/j.jbi.2023.104549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Revised: 10/11/2023] [Accepted: 11/16/2023] [Indexed: 11/22/2023]
Abstract
BACKGROUND Content coverage of patient safety ontology and classification systems should be evaluated to provide a guide for users to select appropriate ones for specific applications. In this review, we identified and compare content coverage of patient safety classifications and ontologies. METHODS We searched different databases and ontology/classification repositories to identify these classifications and ontologies. We included patient safety-related taxonomies, ontologies, classifications, and terminologies. We identified and extracted different concepts covered by these systems and mapped these concepts to international classification for patient safety (ICPS) and finally compared the content of these systems. RESULTS Finally, 89 papers (77 classifications or ontologies) were analyzed. Thirteen classifications have been developed to cover all medical domains. Among specific domain systems, most systems cover medication (16), surgery (8), medical devices (3), general practice (3), and primary care (3). The most common patient safety-related concepts covered in these systems include incident types (41), contributing factors/hazards (31), patient outcomes (29), degree of harm (25), and action (18). However, stage/phase (6), incident characteristics (5), detection (5), people involved (5), organizational outcomes (4), error type (4), and care setting (3) are some of the less covered concepts in these classifications/ontologies. CONCLUSION Among general systems, ICPS, World Health Organization's Adverse Reaction Terminology (WHO-ART), and Ontology of Adverse Events (OAE) cover most patient safety concepts and can be used as a gold standard for all medical domains. As a result, reporting systems could make use of these broad classifications, but the majority of their covered concepts are related to patient outcomes, with the exception of ICPS, which covers other patient safety concepts. However, the ICPS does not cover specialized domain concepts. For specific medical domains, MedDRA, NCC MERP, OPAE, ADRO, PPST, OCCME, TRTE, TSAHI, and PSIC-PC provide the broadest coverage of concepts. Many of the patient safety classifications and ontologies are not formally registered or available as formal classification/ontology in ontology repositories such as BioPortal. This study may be used as a guide for choosing appropriate classifications for various applications or expanding less developed patient safety classifications/ontologies. Furthermore, the same concepts are not represented by the same terms; therefore, the current study could be used to guide a harmonization process for existing or future patient safety classifications/ontologies.
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Affiliation(s)
- Sharare Taheri Moghadam
- Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran
| | - Abbas Sheikhtaheri
- Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran.
| | - Nakysa Hooman
- Aliasghar Clinical Research Development Center (AACRDC), Aliasghar Children Hospital, Department of Pediatrics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
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Deng Y, Tu D, O'Callaghan CJ, Liu G, Xu W. Two-stage multivariate Mendelian randomization on multiple outcomes with mixed distributions. Stat Methods Med Res 2023; 32:1543-1558. [PMID: 37338962 PMCID: PMC10515454 DOI: 10.1177/09622802231181220] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/21/2023]
Abstract
In clinical research, it is important to study whether certain clinical factors or exposures have causal effects on clinical and patient-reported outcomes such as toxicities, quality of life, and self-reported symptoms, which can help improve patient care. Usually, such outcomes are recorded as multiple variables with different distributions. Mendelian randomization (MR) is a commonly used technique for causal inference with the help of genetic instrumental variables to deal with observed and unobserved confounders. Nevertheless, the current methodology of MR for multiple outcomes only focuses on one outcome at a time, meaning that it does not consider the correlation structure of multiple outcomes, which may lead to a loss of statistical power. In situations with multiple outcomes of interest, especially when there are mixed correlated outcomes with different distributions, it is much more desirable to jointly analyze them with a multivariate approach. Some multivariate methods have been proposed to model mixed outcomes; however, they do not incorporate instrumental variables and cannot handle unmeasured confounders. To overcome the above challenges, we propose a two-stage multivariate Mendelian randomization method (MRMO) that can perform multivariate analysis of mixed outcomes using genetic instrumental variables. We demonstrate that our proposed MRMO algorithm can gain power over the existing univariate MR method through simulation studies and a clinical application on a randomized Phase III clinical trial study on colorectal cancer patients.
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Affiliation(s)
- Yangqing Deng
- Department of Biostatistics, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Dongsheng Tu
- Canadian Cancer Trials Group, Queen's University, Kingston, ON, Canada
| | | | - Geoffrey Liu
- Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Medical Oncology and Hematology, Princess Margaret Cancer Centre, Toronto, ON, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Wei Xu
- Department of Biostatistics, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
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9
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Danis D, Jacobsen JOB, Wagner AH, Groza T, Beckwith MA, Rekerle L, Carmody LC, Reese J, Hegde H, Ladewig MS, Seitz B, Munoz-Torres M, Harris NL, Rambla J, Baudis M, Mungall CJ, Haendel MA, Robinson PN. Phenopacket-tools: Building and validating GA4GH Phenopackets. PLoS One 2023; 18:e0285433. [PMID: 37196000 PMCID: PMC10191354 DOI: 10.1371/journal.pone.0285433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 04/21/2023] [Indexed: 05/19/2023] Open
Abstract
The Global Alliance for Genomics and Health (GA4GH) is a standards-setting organization that is developing a suite of coordinated standards for genomics. The GA4GH Phenopacket Schema is a standard for sharing disease and phenotype information that characterizes an individual person or biosample. The Phenopacket Schema is flexible and can represent clinical data for any kind of human disease including rare disease, complex disease, and cancer. It also allows consortia or databases to apply additional constraints to ensure uniform data collection for specific goals. We present phenopacket-tools, an open-source Java library and command-line application for construction, conversion, and validation of phenopackets. Phenopacket-tools simplifies construction of phenopackets by providing concise builders, programmatic shortcuts, and predefined building blocks (ontology classes) for concepts such as anatomical organs, age of onset, biospecimen type, and clinical modifiers. Phenopacket-tools can be used to validate the syntax and semantics of phenopackets as well as to assess adherence to additional user-defined requirements. The documentation includes examples showing how to use the Java library and the command-line tool to create and validate phenopackets. We demonstrate how to create, convert, and validate phenopackets using the library or the command-line application. Source code, API documentation, comprehensive user guide and a tutorial can be found at https://github.com/phenopackets/phenopacket-tools. The library can be installed from the public Maven Central artifact repository and the application is available as a standalone archive. The phenopacket-tools library helps developers implement and standardize the collection and exchange of phenotypic and other clinical data for use in phenotype-driven genomic diagnostics, translational research, and precision medicine applications.
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Affiliation(s)
- Daniel Danis
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, United States of America
| | - Julius O. B. Jacobsen
- William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
| | - Alex H. Wagner
- Departments of Pediatrics and Biomedical Informatics, The Ohio State University College of Medicine, Columbus, OH, United States of America
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children’s Hospital, Columbus, OH, United States of America
| | | | - Martha A. Beckwith
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, United States of America
| | - Lauren Rekerle
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, United States of America
| | - Leigh C. Carmody
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, United States of America
| | - Justin Reese
- Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, United States of America
| | - Harshad Hegde
- Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, United States of America
| | - Markus S. Ladewig
- Department of Ophthalmology, Klinikum Saarbrücken, Saarbrücken, Germany
| | - Berthold Seitz
- Department of Ophthalmology, Saarland University Medical Center, Homburg/Saar, Germany
| | - Monica Munoz-Torres
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, United States of America
| | - Nomi L. Harris
- Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, United States of America
| | - Jordi Rambla
- European Genome-Phenome Archive (EGA) in the Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Michael Baudis
- University of Zurich and Swiss Institute of Bioinformatics, Zurich, Switzerland
| | - Christopher J. Mungall
- Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, United States of America
| | - Melissa A. Haendel
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, United States of America
| | - Peter N. Robinson
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, United States of America
- Institute for Systems Genomics, University of Connecticut, Farmington, CT, United States of America
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10
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Taneja SB, Callahan TJ, Paine MF, Kane-Gill SL, Kilicoglu H, Joachimiak MP, Boyce RD. Developing a Knowledge Graph for Pharmacokinetic Natural Product-Drug Interactions. J Biomed Inform 2023; 140:104341. [PMID: 36933632 PMCID: PMC10150409 DOI: 10.1016/j.jbi.2023.104341] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Revised: 01/09/2023] [Accepted: 03/13/2023] [Indexed: 03/17/2023]
Abstract
BACKGROUND Pharmacokinetic natural product-drug interactions (NPDIs) occur when botanical or other natural products are co-consumed with pharmaceutical drugs. With the growing use of natural products, the risk for potential NPDIs and consequent adverse events has increased. Understanding mechanisms of NPDIs is key to preventing or minimizing adverse events. Although biomedical knowledge graphs (KGs) have been widely used for drug-drug interaction applications, computational investigation of NPDIs is novel. We constructed NP-KG as a first step toward computational discovery of plausible mechanistic explanations for pharmacokinetic NPDIs that can be used to guide scientific research. METHODS We developed a large-scale, heterogeneous KG with biomedical ontologies, linked data, and full texts of the scientific literature. To construct the KG, biomedical ontologies and drug databases were integrated with the Phenotype Knowledge Translator framework. The semantic relation extraction systems, SemRep and Integrated Network and Dynamic Reasoning Assembler, were used to extract semantic predications (subject-relation-object triples) from full texts of the scientific literature related to the exemplar natural products green tea and kratom. A literature-based graph constructed from the predications was integrated into the ontology-grounded KG to create NP-KG. NP-KG was evaluated with case studies of pharmacokinetic green tea- and kratom-drug interactions through KG path searches and meta-path discovery to determine congruent and contradictory information in NP-KG compared to ground truth data. We also conducted an error analysis to identify knowledge gaps and incorrect predications in the KG. RESULTS The fully integrated NP-KG consisted of 745,512 nodes and 7,249,576 edges. Evaluation of NP-KG resulted in congruent (38.98% for green tea, 50% for kratom), contradictory (15.25% for green tea, 21.43% for kratom), and both congruent and contradictory (15.25% for green tea, 21.43% for kratom) information compared to ground truth data. Potential pharmacokinetic mechanisms for several purported NPDIs, including the green tea-raloxifene, green tea-nadolol, kratom-midazolam, kratom-quetiapine, and kratom-venlafaxine interactions were congruent with the published literature. CONCLUSION NP-KG is the first KG to integrate biomedical ontologies with full texts of the scientific literature focused on natural products. We demonstrate the application of NP-KG to identify known pharmacokinetic interactions between natural products and pharmaceutical drugs mediated by drug metabolizing enzymes and transporters. Future work will incorporate context, contradiction analysis, and embedding-based methods to enrich NP-KG. NP-KG is publicly available at https://doi.org/10.5281/zenodo.6814507. The code for relation extraction, KG construction, and hypothesis generation is available at https://github.com/sanyabt/np-kg.
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Affiliation(s)
- Sanya B Taneja
- Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA 15206, USA.
| | - Tiffany J Callahan
- Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA
| | - Mary F Paine
- Department of Pharmaceutical Sciences, College of Pharmacy and Pharmaceutical Sciences, Washington State University, Spokane, WA 99202, USA
| | | | - Halil Kilicoglu
- School of Information Sciences, University of Illinois at Urbana-Champaign, Champaign, IL 61820, USA
| | - Marcin P Joachimiak
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Richard D Boyce
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15206, USA
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11
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Dhrangadhariya A, Müller H. Not so weak PICO: leveraging weak supervision for participants, interventions, and outcomes recognition for systematic review automation. JAMIA Open 2023; 6:ooac107. [PMID: 36632329 PMCID: PMC9828146 DOI: 10.1093/jamiaopen/ooac107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 12/01/2022] [Accepted: 12/21/2022] [Indexed: 01/11/2023] Open
Abstract
Objective The aim of this study was to test the feasibility of PICO (participants, interventions, comparators, outcomes) entity extraction using weak supervision and natural language processing. Methodology We re-purpose more than 127 medical and nonmedical ontologies and expert-generated rules to obtain multiple noisy labels for PICO entities in the evidence-based medicine (EBM)-PICO corpus. These noisy labels are aggregated using simple majority voting and generative modeling to get consensus labels. The resulting probabilistic labels are used as weak signals to train a weakly supervised (WS) discriminative model and observe performance changes. We explore mistakes in the EBM-PICO that could have led to inaccurate evaluation of previous automation methods. Results In total, 4081 randomized clinical trials were weakly labeled to train the WS models and compared against full supervision. The models were separately trained for PICO entities and evaluated on the EBM-PICO test set. A WS approach combining ontologies and expert-generated rules outperformed full supervision for the participant entity by 1.71% macro-F1. Error analysis on the EBM-PICO subset revealed 18-23% erroneous token classifications. Discussion Automatic PICO entity extraction accelerates the writing of clinical systematic reviews that commonly use PICO information to filter health evidence. However, PICO extends to more entities-PICOS (S-study type and design), PICOC (C-context), and PICOT (T-timeframe) for which labelled datasets are unavailable. In such cases, the ability to use weak supervision overcomes the expensive annotation bottleneck. Conclusions We show the feasibility of WS PICO entity extraction using freely available ontologies and heuristics without manually annotated data. Weak supervision has encouraging performance compared to full supervision but requires careful design to outperform it.
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Affiliation(s)
- Anjani Dhrangadhariya
- Corresponding Author: Anjani Dhrangadhariya, MSc, Institute of Informatics, University of Applied Sciences Western Switzerland (HES-SO), Rue de Technopôle 3, 3960 Sierre, Switzerland;
| | - Henning Müller
- Institute of Informatics, University of Applied Sciences Western Switzerland (HES-SO), Sierre, Switzerland,University of Geneva (UNIGE), Geneva, Switzerland
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12
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Zi W, Yang Q, Su J, He Y, Xie J. OAE-based data mining and modeling analysis of adverse events associated with three licensed HPV vaccines. Heliyon 2022; 8:e11515. [DOI: 10.1016/j.heliyon.2022.e11515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Revised: 06/11/2022] [Accepted: 11/03/2022] [Indexed: 11/13/2022] Open
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13
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Pavel A, Saarimäki LA, Möbus L, Federico A, Serra A, Greco D. The potential of a data centred approach & knowledge graph data representation in chemical safety and drug design. Comput Struct Biotechnol J 2022; 20:4837-4849. [PMID: 36147662 PMCID: PMC9464643 DOI: 10.1016/j.csbj.2022.08.061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 08/26/2022] [Accepted: 08/26/2022] [Indexed: 11/20/2022] Open
Abstract
Big Data pervades nearly all areas of life sciences, yet the analysis of large integrated data sets remains a major challenge. Moreover, the field of life sciences is highly fragmented and, consequently, so is its data, knowledge, and standards. This, in turn, makes integrated data analysis and knowledge gathering across sub-fields a demanding task. At the same time, the integration of various research angles and data types is crucial for modelling the complexity of organisms and biological processes in a holistic manner. This is especially valid in the context of drug development and chemical safety assessment where computational methods can provide solutions for the urgent need of fast, effective, and sustainable approaches. At the same time, such computational methods require the development of methodologies suitable for an integrated and data centred Big Data view. Here we discuss Knowledge Graphs (KG) as a solution to a data centred analysis approach for drug and chemical development and safety assessment. KGs are knowledge bases, data analysis engines, and knowledge discovery systems all in one, allowing them to be used from simple data retrieval, over meta-analysis to complex predictive and knowledge discovery systems. Therefore, KGs have immense potential to advance the data centred approach, the re-usability, and informativity of data. Furthermore, they can improve the power of analysis, and the complexity of modelled processes, all while providing knowledge in a natively human understandable network data model.
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Affiliation(s)
- Alisa Pavel
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.,BioMediTech Institute, Tampere University, Tampere, Finland.,Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Tampere, Finland
| | - Laura A Saarimäki
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.,BioMediTech Institute, Tampere University, Tampere, Finland.,Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Tampere, Finland
| | - Lena Möbus
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.,BioMediTech Institute, Tampere University, Tampere, Finland.,Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Tampere, Finland
| | - Antonio Federico
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.,BioMediTech Institute, Tampere University, Tampere, Finland.,Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Tampere, Finland
| | - Angela Serra
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.,BioMediTech Institute, Tampere University, Tampere, Finland.,Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Tampere, Finland
| | - Dario Greco
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.,BioMediTech Institute, Tampere University, Tampere, Finland.,Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Tampere, Finland.,Institute of Biotechnology, University of Helsinki, Helsinki, Finland
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14
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The Representation of Causality and Causation with Ontologies: A Systematic Literature Review. Online J Public Health Inform 2022; 14:e4. [PMID: 36120162 PMCID: PMC9473331 DOI: 10.5210/ojphi.v14i1.12577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
Abstract
Objective To explore how disease-related causality is formally represented in current ontologies and identify their potential limitations. Methods We conducted a systematic literature search on eight databases (PubMed, Institute of Electrical and Electronic Engendering (IEEE Xplore), Association for Computing Machinery (ACM), Scopus, Web of Science databases, Ontobee, OBO Foundry, and Bioportal. We included studies published between January 1, 1970, and December 9, 2020, that formally represent the notions of causality and causation in the medical domain using ontology as a representational tool. Further inclusion criteria were publication in English and peer-reviewed journals or conference proceedings. Two authors (SS, RM) independently assessed study quality and performed content analysis using a modified validated extraction grid with pre-established categorization. Results The search strategy led to a total of 8,501 potentially relevant papers, of which 50 met the inclusion criteria. Only 14 out of 50 (28%) specified the nature of causation, and only 7 (14%) included clear and non-circular natural language definitions. Although several theories of causality were mentioned, none of the articles offers a widely accepted conceptualization of how causation and causality can be formally represented. Conclusion No current ontology captures the wealth of available concepts of causality. This provides an opportunity for the development of a formal ontology of causation/causality.
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15
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Patient safety classification, taxonomy and ontology systems: A systematic review on development and evaluation methodologies. J Biomed Inform 2022; 133:104150. [PMID: 35878822 DOI: 10.1016/j.jbi.2022.104150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 06/11/2022] [Accepted: 07/19/2022] [Indexed: 11/24/2022]
Abstract
INTRODUCTION Patient safety classifications/ontologies enable patient safety information systems to receive and analyze patient safety data to improve patient safety. Patient safety classifications/ontologies have been developed and evaluated using a variety of methods. The purpose of this review was to discuss and analyze the methodologies for developing and evaluating patient safety classifications/ontologies. METHODS Studies that developed or evaluated patient safety classifications, terminologies, taxonomies, or ontologies were searched through Google Scholar, Google search engines, National Center for Biomedical Ontology (NCBO) BioPortal, Open Biological and Biomedical Ontology (OBO) Foundry and World Health Organization (WHO) websites and Scopus, Web of Science, PubMed, and Science Direct. We updated our search on 30 February 2021 and included all studies published until the end of 2020. Studies that developed or evaluated classifications only for patient safety and provided information on how they were developed or evaluated were included. Systems with covered patient safety terms (such as ICD-10) but are not specifically developed for patient safety were excluded. The quality and the risk of bias of studies were not assessed because all methodologies and criteria were intended to be covered. In addition, we analyzed the data through descriptive narrative synthesis and compared and classified the development and evaluation methods and evaluation criteria according to available development and evaluation approaches for biomedical ontologies. RESULTS We identified 84 articles that met all of the inclusion criteria, resulting in 70 classifications/ontologies, nine of which were for the general medical domain. The most papers were published in 2010 and 2011, with 8 and 7 papers, respectively. The United States (50) and Australia (23) have the most studies. The most commonly used methods for developing classifications/ontologies included the use of existing systems (for expanding or mapping) (44) and qualitative analysis of event reports (39). The most common evaluation methods were coding or classifying some safety report samples (25), quantitative analysis of incidents based on the developed classification (24), and consensus among physicians (16). The most commonly applied evaluation criteria were reliability (27), content and face validity (9), comprehensiveness (6), usability (5), linguistic clarity (5), and impact (4), respectively. CONCLUSIONS Because of the weaknesses and strengths of the development/evaluation methods, it is advised that more than one method for development or evaluation, as well as evaluation criteria, should be used. To organize the processes of developing classification/ontologies, well-established approaches such as Methontology are recommended. The most prevalent evaluation methods applied in this domain are well fitted to the biomedical ontology evaluation methods, but it is also advised to apply some evaluation approaches such as logic, rules, and Natural language processing (NLP) based in combination with other evaluation approaches. This research can assist domain researchers in developing or evaluating domain ontologies using more complete methodologies. There is also a lack of reporting consistency in the literature and same methods or criteria were reported with different terminologies.
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16
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Huffman A, Ong E, Hur J, D’Mello A, Tettelin H, He Y. COVID-19 vaccine design using reverse and structural vaccinology, ontology-based literature mining and machine learning. Brief Bioinform 2022; 23:bbac190. [PMID: 35649389 PMCID: PMC9294427 DOI: 10.1093/bib/bbac190] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Revised: 04/13/2022] [Accepted: 04/26/2022] [Indexed: 12/11/2022] Open
Abstract
Rational vaccine design, especially vaccine antigen identification and optimization, is critical to successful and efficient vaccine development against various infectious diseases including coronavirus disease 2019 (COVID-19). In general, computational vaccine design includes three major stages: (i) identification and annotation of experimentally verified gold standard protective antigens through literature mining, (ii) rational vaccine design using reverse vaccinology (RV) and structural vaccinology (SV) and (iii) post-licensure vaccine success and adverse event surveillance and its usage for vaccine design. Protegen is a database of experimentally verified protective antigens, which can be used as gold standard data for rational vaccine design. RV predicts protective antigen targets primarily from genome sequence analysis. SV refines antigens through structural engineering. Recently, RV and SV approaches, with the support of various machine learning methods, have been applied to COVID-19 vaccine design. The analysis of post-licensure vaccine adverse event report data also provides valuable results in terms of vaccine safety and how vaccines should be used or paused. Ontology standardizes and incorporates heterogeneous data and knowledge in a human- and computer-interpretable manner, further supporting machine learning and vaccine design. Future directions on rational vaccine design are discussed.
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Affiliation(s)
- Anthony Huffman
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, Michigan 48109, USA
| | - Edison Ong
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, Michigan 48109, USA
| | - Junguk Hur
- Department of Biomedical Sciences, University of North Dakota School of Medicine and Health Sciences, Grand Forks, North Dakota 58202, USA
| | - Adonis D’Mello
- Department of Microbiology and Immunology, Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - Hervé Tettelin
- Department of Microbiology and Immunology, Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - Yongqun He
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, Michigan 48109, USA
- Unit for Laboratory Animal Medicine, Department of Microbiology and Immunology, Center for Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, Michigan 48109, USA
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17
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Guo W, Deguise J, Tian Y, Huang PCE, Goru R, Yang Q, Peng S, Zhang L, Zhao L, Xie J, He Y. Profiling COVID-19 Vaccine Adverse Events by Statistical and Ontological Analysis of VAERS Case Reports. Front Pharmacol 2022; 13:870599. [PMID: 35814246 PMCID: PMC9263450 DOI: 10.3389/fphar.2022.870599] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 05/23/2022] [Indexed: 12/28/2022] Open
Abstract
Since the beginning of the COVID-19 pandemic, vaccines have been developed to mitigate the spread of SARS-CoV-2, the virus that causes COVID-19. These vaccines have been effective in reducing the rate and severity of COVID-19 infection but also have been associated with various adverse events (AEs). In this study, data from the Vaccine Adverse Event Reporting System (VAERS) was queried and analyzed via the Cov19VaxKB vaccine safety statistical analysis tool to identify statistically significant (i.e., enriched) AEs for the three currently FDA-authorized or approved COVID-19 vaccines. An ontology-based classification and literature review were conducted for these enriched AEs. Using VAERS data as of 31 December 2021, 96 AEs were found to be statistically significantly associated with the Pfizer-BioNTech, Moderna, and/or Janssen COVID-19 vaccines. The Janssen COVID-19 vaccine had a higher crude reporting rate of AEs compared to the Moderna and Pfizer COVID-19 vaccines. Females appeared to have a higher case report frequency for top adverse events compared to males. Using the Ontology of Adverse Event (OAE), these 96 adverse events were classified to different categories such as behavioral and neurological AEs, cardiovascular AEs, female reproductive system AEs, and immune system AEs. Further statistical comparison between different ages, doses, and sexes was also performed for three notable AEs: myocarditis, GBS, and thrombosis. The Pfizer vaccine was found to have a closer association with myocarditis than the other two COVID-19 vaccines in VAERS, while the Janssen vaccine was more likely to be associated with thrombosis and GBS AEs. To support standard AE representation and study, we have also modeled and classified the newly identified thrombosis with thrombocytopenia syndrome (TTS) AE and its subclasses in the OAE by incorporating the Brighton Collaboration definition. Notably, severe COVID-19 vaccine AEs (including myocarditis, GBS, and TTS) rarely occur in comparison to the large number of COVID-19 vaccinations administered in the United States, affirming the overall safety of these COVID-19 vaccines.
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Affiliation(s)
- Wenxin Guo
- College of Literature, Science and the Arts, University of Michigan, Ann Arbor, MI, United States
| | - Jessica Deguise
- College of Literature, Science and the Arts, University of Michigan, Ann Arbor, MI, United States
| | - Yujia Tian
- Department of Cell Biology and Neuroscience, Rutgers University, New Brunswick, NJ, United States
| | - Philip Chi-En Huang
- College of Literature, Science and the Arts, University of Michigan, Ann Arbor, MI, United States
| | - Rohit Goru
- College of Literature, Science and the Arts, University of Michigan, Ann Arbor, MI, United States
| | - Qiuyue Yang
- Chongqing Engineering Research Center of Medical Electronics and Information Technology, School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Suyuan Peng
- National Institute of Health Data Science, Peking University, Beijing, China
| | - Luxia Zhang
- National Institute of Health Data Science, Peking University, Beijing, China
- Department of Medicine, Renal Division, Peking University First Hospital, Peking University Institute of Nephrology, Beijing, China
- Advanced Institute of Information Technology, Peking University, Hangzhou, China
| | - Lili Zhao
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, United States
| | - Jiangan Xie
- Chongqing Engineering Research Center of Medical Electronics and Information Technology, School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing, China
- *Correspondence: Jiangan Xie, ; Yongqun He,
| | - Yongqun He
- Unit for Laboratory Animal Medicine, University of Michigan Medical School, Ann Arbor, MI, United States
- Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI, United States
- Center of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, United States
- *Correspondence: Jiangan Xie, ; Yongqun He,
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18
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Umberfield EE, Stansbury C, Ford K, Jiang Y, Kardia SLR, Thomer AK, Harris MR. Evaluating and Extending the Informed Consent Ontology for Representing Permissions from the Clinical Domain. APPLIED ONTOLOGY 2022; 17:321-336. [PMID: 36312514 PMCID: PMC9616177 DOI: 10.3233/ao-210260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
The purpose of this study was to evaluate, revise, and extend the Informed Consent Ontology (ICO) for expressing clinical permissions, including reuse of residual clinical biospecimens and health data. This study followed a formative evaluation design and used a bottom-up modeling approach. Data were collected from the literature on US federal regulations and a study of clinical consent forms. Eleven federal regulations and fifteen permission-sentences from clinical consent forms were iteratively modeled to identify entities and their relationships, followed by community reflection and negotiation based on a series of predetermined evaluation questions. ICO included fifty-two classes and twelve object properties necessary when modeling, demonstrating appropriateness of extending ICO for the clinical domain. Twenty-six additional classes were imported into ICO from other ontologies, and twelve new classes were recommended for development. This work addresses a critical gap in formally representing permissions clinical permissions, including reuse of residual clinical biospecimens and health data. It makes missing content available to the OBO Foundry, enabling use alongside other widely-adopted biomedical ontologies. ICO serves as a machine-interpretable and interoperable tool for responsible reuse of residual clinical biospecimens and health data at scale.
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Affiliation(s)
- Elizabeth E. Umberfield
- Indiana University Richard M Fairbanks School of Public Health, Health Policy & Management; Indianapolis, IN, USA
- Regenstrief Institute Inc, Center for Biomedical Informatics, Indianapolis, IN, USA
| | - Cooper Stansbury
- University of Michigan Medical School, Computational Medicine and Bioinformatics; Ann Arbor, MI, USA
- University of Michigan, Institute for Computational Discovery & Engineering; Ann Arbor, MI, USA
| | | | - Yun Jiang
- University of Michigan School of Nursing, Systems, Populations and Leadership; Ann Arbor, MI, USA
| | - Sharon L. R. Kardia
- University of Michigan School of Public Health, Epidemiology; Ann Arbor, MI, USA
| | - Andrea K. Thomer
- University of Michigan School of Information, Ann Arbor, MI, USA
| | - Marcelline R. Harris
- University of Michigan School of Nursing, Systems, Populations and Leadership; Ann Arbor, MI, USA
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19
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He Y. Development and Applications of Interoperable Biomedical Ontologies for Integrative Data and Knowledge Representation and Multiscale Modeling in Systems Medicine. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2022; 2486:233-244. [PMID: 35437726 DOI: 10.1007/978-1-0716-2265-0_12] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
The data FAIR Guiding Principles state that all data should be Findable, Accessible, Interoperable, and Reusable. Ontology is critical to data integration, sharing, and analysis. Given thousands of ontologies have been developed in the era of artificial intelligence, it is critical to have interoperable ontologies to support standardized data and knowledge presentation and reasoning. For interoperable ontology development, the eXtensible ontology development (XOD) strategy offers four principles including ontology term reuse, semantic alignment, ontology design pattern usage, and community extensibility. Many software programs are available to help implement these principles. As a demonstration, the XOD strategy is applied to developing the interoperable Coronavirus Infectious Disease Ontology (CIDO). Various applications of interoperable ontologies, such as COVID-19 and kidney precision medicine research, are also introduced in this chapter.
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Affiliation(s)
- Yongqun He
- Unit for Laboratory Animal Medicine, Department of Microbiology and Immunology, Center of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA.
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Ngai J, Kalter M, Byrd JB, Racz R, He Y. Ontology-Based Classification and Analysis of Adverse Events Associated With the Usage of Chloroquine and Hydroxychloroquine. Front Pharmacol 2022; 13:812338. [PMID: 35401219 PMCID: PMC8983871 DOI: 10.3389/fphar.2022.812338] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Accepted: 03/07/2022] [Indexed: 12/20/2022] Open
Abstract
Multiple methodologies have been developed to identify and predict adverse events (AEs); however, many of these methods do not consider how patient population characteristics, such as diseases, age, and gender, affect AEs seen. In this study, we evaluated the utility of collecting and analyzing AE data related to hydroxychloroquine (HCQ) and chloroquine (CQ) from US Prescribing Information (USPIs, also called drug product labels or package inserts), the FDA Adverse Event Reporting System (FAERS), and peer-reviewed literature from PubMed/EMBASE, followed by AE classification and modeling using the Ontology of Adverse Events (OAE). Our USPI analysis showed that CQ and HCQ AE profiles were similar, although HCQ was reported to be associated with fewer types of cardiovascular, nervous system, and musculoskeletal AEs. According to EMBASE literature mining, CQ and HCQ were associated with QT prolongation (primarily when treating COVID-19), heart arrhythmias, development of Torsade des Pointes, and retinopathy (primarily when treating lupus). The FAERS data was analyzed by proportional ratio reporting, Chi-square test, and minimal case number filtering, followed by OAE classification. HCQ was associated with 63 significant AEs (including 21 cardiovascular AEs) for COVID-19 patients and 120 significant AEs (including 12 cardiovascular AEs) for lupus patients, supporting the hypothesis that the disease being treated affects the type and number of certain CQ/HCQ AEs that are manifested. Using an HCQ AE patient example reported in the literature, we also ontologically modeled how an AE occurs and what factors (e.g., age, biological sex, and medical history) are involved in the AE formation. The methodology developed in this study can be used for other drugs and indications to better identify patient populations that are particularly vulnerable to AEs.
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Affiliation(s)
- Jamie Ngai
- College of Pharmacy, University of Michigan, Ann Arbor, MI, United States
| | - Madison Kalter
- College of Literature, Science, and Arts, University of Michigan, Ann Arbor, MI, United States
| | - James Brian Byrd
- Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan Medical School, Ann Arbor, MI, United States
| | - Rebecca Racz
- Division of Applied Regulatory Science, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, United States
| | - Yongqun He
- Unit for Laboratory Animal Medicine, University of Michigan Medical School, Ann Arbor, MI, United States.,Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI, United States.,Center for Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, United States
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21
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Huang PC, Goru R, Huffman A, Yu Lin A, Cooke MF, He Y. Cov19VaxKB: A Web-based Integrative COVID-19 Vaccine Knowledge Base. Vaccine X 2021; 10:100139. [PMID: 34981039 PMCID: PMC8716025 DOI: 10.1016/j.jvacx.2021.100139] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2021] [Revised: 11/09/2021] [Accepted: 12/22/2021] [Indexed: 12/23/2022] Open
Abstract
The development of SARS-CoV-2 vaccines during the COVID-19 pandemic has prompted the emergence of COVID-19 vaccine data. Timely access to COVID-19 vaccine information is crucial to researchers and public. To support more comprehensive annotation, integration, and analysis of COVID-19 vaccine information, we have developed Cov19VaxKB, a knowledge-focused COVID-19 vaccine database (http://www.violinet.org/cov19vaxkb/). Cov19VaxKB features comprehensive lists of COVID-19 vaccines, vaccine formulations, clinical trials, publications, news articles, and vaccine adverse event case reports. A web-based query interface enables comparison of product information and host responses among various vaccines. The knowledge base also includes a vaccine design tool for predicting vaccine targets and a statistical analysis tool that identifies enriched adverse events for FDA-authorized COVID-19 vaccines based on VAERS case report data. To support data exchange, Cov19VaxKB is synchronized with Vaccine Ontology and the Vaccine Investigation and Online Information Network (VIOLIN) database. The data integration and analytical features of Cov19VaxKB can facilitate vaccine research and development while also serving as a useful reference for the public.
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Key Words
- AE, adverse event
- CDC, Centers for Disease Control and Prevention
- COVID-19
- COVID-19 vaccine
- COVID-19, Coronavirus disease 2019
- Cov19VaxKB
- FDA, Food and Drug Administration
- MERS-CoV, Middle Eastern Respiratory Syndrome
- NCBI, National Center for Biotechnology Information
- OWL, Web Ontology Language
- PMID, PubMed identification number
- PRR, Proportional Reporting Ratio
- SARS-CoV, Severe Acute Respiratory Syndrome Coronavirus
- SARS-CoV-2
- SARS-CoV-2, Severe Acute Respiratory Syndrome Coronavirus 2
- VAERS
- VAERS, Vaccine Adverse Event Reporting System
- VIOLIN, Vaccine Investigation and Online Information Network
- VO, Vaccine Ontology
- WHO, World Health Organization
- adverse event
- bioinformatics
- database
- knowledge base
- ontology
- vaccine
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Affiliation(s)
- Philip C. Huang
- College of Literature, Science, and the Arts, University of Michigan, Ann Arbor, MI 48109, USA
| | - Rohit Goru
- College of Literature, Science, and the Arts, University of Michigan, Ann Arbor, MI 48109, USA
| | - Anthony Huffman
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Asiyah Yu Lin
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Michael F. Cooke
- School of Information, University of Michigan, Ann Arbor, MI 48109, USA
| | - Yongqun He
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
- Unit for Laboratory Animal Medicine, University of Michigan Medical School, Ann Arbor, MI 48109, USA
- Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI 48109, USA
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22
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Xiao Y, Zheng X, Song W, Tong F, Mao Y, Liu S, Zhao D. CIDO-COVID-19: An Ontology for COVID-19 Based on CIDO. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:2119-2122. [PMID: 34891707 DOI: 10.1109/embc46164.2021.9629555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
To realize integration, organization and reusability of knowledge related to COVID-19, an ontology for COVID-19 (CIDO-COVID-19) was constructed which extended the Coronavirus Infectious Disease Ontology (CIDO) by adding terms of COVID-19 related to symptoms, prevention, drugs and clinical domains. First, terms from the existing ontologies, literature, clinical guidelines and other resources about COVID-19 were merged. Then, the Stanford seven-step approach was used to define and organize the acquired terms. Finally, the CIDO-COVID-19 was built on basis of the terms mentioned above using Protégé. The CIDO-COVID-19 is a more comprehensive ontology for COVID-19, covering multiple areas in the domain of COVID-19, including disease, diagnosis, etiology, virus, transmission, symptom, treatment, drug and prevention.Clinical Relevance- The CIDO-COVID-19 covers multiple areas related to COVID-19, including diseases, diagnosis, etiology, virus, transmission, symptoms, treatment, drugs, prevention. Compared with the CIDO, it is expanded to cover drugs, prevention, and clinical domain. The definition of terms in CIDO-COVID-19 refers to biomedical ontologies, Clinical glossaries and clinical guidelines for COVID-19, which can provide clinicians with standard terminology in the clinical domain.
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Zhu Y, Liu L, Gao B, Liu J, Qiao X, Lian C, He Y. TCDO: A Community-Based Ontology for Integrative Representation and Analysis of Traditional Chinese Drugs and Their Properties. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE : ECAM 2021; 2021:6637810. [PMID: 34603473 PMCID: PMC8483929 DOI: 10.1155/2021/6637810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Revised: 08/04/2021] [Accepted: 08/31/2021] [Indexed: 11/17/2022]
Abstract
Traditional Chinese drugs (TCDs) have been widely used in clinical practice in China and many other regions for thousands of years. Nowadays TCD's bioactive ingredients and mechanisms of action are being identified. However, the lack of standardized terminologies or ontologies for the description of TCDs has hindered the interoperability and deep analysis of TCD knowledge and data. By aligning with the Basic Formal Ontology (BFO), an ISO-approved top-level ontology, we constructed a community-driven TCD ontology (TCDO) with the aim of supporting standardized TCD representation and integrated analysis. TCDO provides logical and textual definitions of TCDs, TCD categories, and the properties of TCDs (i.e., nature, flavor, toxicity, and channel tropism). More than 400 popular TCD decoction pieces (TCD-DPs) and Chinese medicinal materials (CMMs) are systematically represented. The logical TCD representation in TCDO supports computer-assisted reasoning and queries using tools such as Description Logic (DL) and SPARQL queries. Our statistical analysis of the knowledge represented in TCDO revealed scientific insights about TCDs. A total of 36 TCDs with medium or high toxicity are most densely distributed, primarily in Aconitum genus, Lamiids clade, and Fabids clade. TCD toxicity is mostly associated with the hot nature and pungent or bitter flavors and has liver, kidney, and spleen channel tropism. The three pairs of TCD flavor-nature associations (i.e., bitter-cold, pungent-warm, and sweet-neutral) were identified. The significance of these findings is discussed. TCDO has also been used to support the development of a web-based traditional Chinese medicine semantic annotation system that provides comprehensive annotation for individual TCDs. As a novel formal TCD ontology, TCDO lays out a strong foundation for more advanced TCD studies in the future.
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Affiliation(s)
- Yan Zhu
- Institute of Information on Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Lihong Liu
- Institute of Information on Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Bo Gao
- Institute of Information on Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Jing Liu
- Institute of Information on Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Xingchao Qiao
- Institute of Information on Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Chaojie Lian
- National Institutes for Food and Drug Control, Beijing 102627, China
| | - Yongqun He
- University of Michigan Medical School, Ann Arbor, MI 48109, USA
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24
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Wang Z, He Y. Precision omics data integration and analysis with interoperable ontologies and their application for COVID-19 research. Brief Funct Genomics 2021; 20:235-248. [PMID: 34159360 PMCID: PMC8287950 DOI: 10.1093/bfgp/elab029] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 05/10/2021] [Accepted: 05/24/2021] [Indexed: 12/12/2022] Open
Abstract
Omics technologies are widely used in biomedical research. Precision medicine focuses on individual-level disease treatment and prevention. Here, we propose the usage of the term 'precision omics' to represent the combinatorial strategy that applies omics to translate large-scale molecular omics data for precision disease understanding and accurate disease diagnosis, treatment and prevention. Given the complexity of both omics and precision medicine, precision omics requires standardized representation and integration of heterogeneous data types. Ontology has emerged as an important artificial intelligence component to become critical for standard data and metadata representation, standardization and integration. To support precision omics, we propose a precision omics ontology hypothesis, which hypothesizes that the effectiveness of precision omics is positively correlated with the interoperability of ontologies used for data and knowledge integration. Therefore, to make effective precision omics studies, interoperable ontologies are required to standardize and incorporate heterogeneous data and knowledge in a human- and computer-interpretable manner. Methods for efficient development and application of interoperable ontologies are proposed and illustrated. With the interoperable omics data and knowledge, omics tools such as OmicsViz can also be evolved to process, integrate, visualize and analyze various omics data, leading to the identification of new knowledge and hypotheses of molecular mechanisms underlying the outcomes of diseases such as COVID-19. Given extensive COVID-19 omics research, we propose the strategy of precision omics supported by interoperable ontologies, accompanied with ontology-based semantic reasoning and machine learning, leading to systematic disease mechanism understanding and rational design of precision treatment and prevention. SHORT ABSTRACT Precision medicine focuses on individual-level disease treatment and prevention. Precision omics is a new strategy that applies omics for precision medicine research, which requires standardized representation and integration of individual genetics and phenotypes, experimental conditions, and data analysis settings. Ontology has emerged as an important artificial intelligence component to become critical for standard data and metadata representation, standardization and integration. To support precision omics, interoperable ontologies are required in order to standardize and incorporate heterogeneous data and knowledge in a human- and computer-interpretable manner. With the interoperable omics data and knowledge, omics tools such as OmicsViz can also be evolved to process, integrate, visualize and analyze various omics data, leading to the identification of new knowledge and hypotheses of molecular mechanisms underlying disease outcomes. The precision COVID-19 omics study is provided as the primary use case to illustrate the rationale and implementation of the precision omics strategy.
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Affiliation(s)
| | - Yongqun He
- University of Michigan Medical School, Ann Arbor, MI, USA
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Wang Z, An J, Lin H, Zhou J, Liu F, Chen J, Duan H, Deng N. Pathway-Driven Coordinated Telehealth System for Management of Patients With Single or Multiple Chronic Diseases in China: System Development and Retrospective Study. JMIR Med Inform 2021; 9:e27228. [PMID: 33998999 PMCID: PMC8167615 DOI: 10.2196/27228] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2021] [Revised: 02/22/2021] [Accepted: 04/16/2021] [Indexed: 11/13/2022] Open
Abstract
Background Integrated care enhanced with information technology has emerged as a means to transform health services to meet the long-term care needs of patients with chronic diseases. However, the feasibility of applying integrated care to the emerging “three-manager” mode in China remains to be explored. Moreover, few studies have attempted to integrate multiple types of chronic diseases into a single system. Objective The aim of this study was to develop a coordinated telehealth system that addresses the existing challenges of the “three-manager” mode in China while supporting the management of single or multiple chronic diseases. Methods The system was designed based on a tailored integrated care model. The model was constructed at the individual scale, mainly focusing on specifying the involved roles and responsibilities through a universal care pathway. A custom ontology was developed to represent the knowledge contained in the model. The system consists of a service engine for data storage and decision support, as well as different forms of clients for care providers and patients. Currently, the system supports management of three single chronic diseases (hypertension, type 2 diabetes mellitus, and chronic obstructive pulmonary disease) and one type of multiple chronic conditions (hypertension with type 2 diabetes mellitus). A retrospective study was performed based on the long-term observational data extracted from the database to evaluate system usability, treatment effect, and quality of care. Results The retrospective analysis involved 6964 patients with chronic diseases and 249 care providers who have registered in our system since its deployment in 2015. A total of 519,598 self-monitoring records have been submitted by the patients. The engine could generate different types of records regularly based on the specific care pathway. Results of the comparison tests and causal inference showed that a part of patient outcomes improved after receiving management through the system, especially the systolic blood pressure of patients with hypertension (P<.001 in all comparison tests and an approximately 5 mmHg decrease after intervention via causal inference). A regional case study showed that the work efficiency of care providers differed among individuals. Conclusions Our system has potential to provide effective management support for single or multiple chronic conditions simultaneously. The tailored closed-loop care pathway was feasible and effective under the “three-manager” mode in China. One direction for future work is to introduce advanced artificial intelligence techniques to construct a more personalized care pathway.
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Affiliation(s)
- Zheyu Wang
- Ministry of Education Key Laboratory of Biomedical Engineering, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Jiye An
- Ministry of Education Key Laboratory of Biomedical Engineering, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Hui Lin
- Ministry of Education Key Laboratory of Biomedical Engineering, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Jiaqiang Zhou
- Sir Run-Run Shaw Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China
| | - Fang Liu
- General Hospital of Ningxia Medical University, Yinchuan, China
| | - Juan Chen
- General Hospital of Ningxia Medical University, Yinchuan, China
| | - Huilong Duan
- Ministry of Education Key Laboratory of Biomedical Engineering, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Ning Deng
- Ministry of Education Key Laboratory of Biomedical Engineering, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
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26
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Xie J, Zi W, Li Z, He Y. Ontology-based Precision Vaccinology for Deep Mechanism Understanding and Precision Vaccine Development. Curr Pharm Des 2021; 27:900-910. [PMID: 33238868 DOI: 10.2174/1381612826666201125112131] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2020] [Accepted: 10/08/2020] [Indexed: 11/22/2022]
Abstract
Vaccination is one of the most important innovations in human history. It has also become a hot research area in a new application - the development of new vaccines against non-infectious diseases such as cancers. However, effective and safe vaccines still do not exist for many diseases, and where vaccines exist, their protective immune mechanisms are often unclear. Although licensed vaccines are generally safe, various adverse events, and sometimes severe adverse events, still exist for a small population. Precision medicine tailors medical intervention to the personal characteristics of individual patients or sub-populations of individuals with similar immunity-related characteristics. Precision vaccinology is a new strategy that applies precision medicine to the development, administration, and post-administration analysis of vaccines. Several conditions contribute to make this the right time to embark on the development of precision vaccinology. First, the increased level of research in vaccinology has generated voluminous "big data" repositories of vaccinology data. Secondly, new technologies such as multi-omics and immunoinformatics bring new methods for investigating vaccines and immunology. Finally, the advent of AI and machine learning software now makes possible the marriage of Big Data to the development of new vaccines in ways not possible before. However, something is missing in this marriage, and that is a common language that facilitates the correlation, analysis, and reporting nomenclature for the field of vaccinology. Solving this bioinformatics problem is the domain of applied biomedical ontology. Ontology in the informatics field is human- and machine-interpretable representation of entities and the relations among entities in a specific domain. The Vaccine Ontology (VO) and Ontology of Vaccine Adverse Events (OVAE) have been developed to support the standard representation of vaccines, vaccine components, vaccinations, host responses, and vaccine adverse events. Many other biomedical ontologies have also been developed and can be applied in vaccine research. Here, we review the current status of precision vaccinology and how ontological development will enhance this field, and propose an ontology-based precision vaccinology strategy to support precision vaccine research and development.
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Affiliation(s)
- Jiangan Xie
- Chongqing Engineering Research Center of Medical Electronics and Information Technology, School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Wenrui Zi
- Chongqing engineering research center of medical electronics and information technology, School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Zhangyong Li
- Chongqing engineering research center of medical electronics and information technology, School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Yongqun He
- Unit of Laboratory Animal Medicine, Development of Microbiology and Immunology, Center of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, Michigan, United States
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27
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Hochheiser H, Jing X, Garcia EA, Ayvaz S, Sahay R, Dumontier M, Banda JM, Beyan O, Brochhausen M, Draper E, Habiel S, Hassanzadeh O, Herrero-Zazo M, Hocum B, Horn J, LeBaron B, Malone DC, Nytrø Ø, Reese T, Romagnoli K, Schneider J, Zhang L(Y, Boyce RD. A Minimal Information Model for Potential Drug-Drug Interactions. Front Pharmacol 2021; 11:608068. [PMID: 33762928 PMCID: PMC7982727 DOI: 10.3389/fphar.2020.608068] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Accepted: 12/29/2020] [Indexed: 01/22/2023] Open
Abstract
Despite the significant health impacts of adverse events associated with drug-drug interactions, no standard models exist for managing and sharing evidence describing potential interactions between medications. Minimal information models have been used in other communities to establish community consensus around simple models capable of communicating useful information. This paper reports on a new minimal information model for describing potential drug-drug interactions. A task force of the Semantic Web in Health Care and Life Sciences Community Group of the World-Wide Web consortium engaged informaticians and drug-drug interaction experts in in-depth examination of recent literature and specific potential interactions. A consensus set of information items was identified, along with example descriptions of selected potential drug-drug interactions (PDDIs). User profiles and use cases were developed to demonstrate the applicability of the model. Ten core information items were identified: drugs involved, clinical consequences, seriousness, operational classification statement, recommended action, mechanism of interaction, contextual information/modifying factors, evidence about a suspected drug-drug interaction, frequency of exposure, and frequency of harm to exposed persons. Eight best practice recommendations suggest how PDDI knowledge artifact creators can best use the 10 information items when synthesizing drug interaction evidence into artifacts intended to aid clinicians. This model has been included in a proposed implementation guide developed by the HL7 Clinical Decision Support Workgroup and in PDDIs published in the CDS Connect repository. The complete description of the model can be found at https://w3id.org/hclscg/pddi.
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Affiliation(s)
- Harry Hochheiser
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, United States
- Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, United States
| | - Xia Jing
- Department of Public Health Sciences, Clemson University, Clemson, SC, United States
| | | | - Serkan Ayvaz
- Department of Software Engineering, Bahçeşehir University, Istanbul, Turkey
| | - Ratnesh Sahay
- Clinical Data Science, AstraZeneca, Cambridge, United Kingdom
| | - Michel Dumontier
- Institute of Data Science, Maastricht University, Maastricht, Netherlands
| | - Juan M. Banda
- Department of Computer Science, Georgia State University, Atlanta, GA, United States
| | - Oya Beyan
- Fraunhofer Institute for Applied Information Technology, RWTH Aachen University, Aachen, Germany
| | - Mathias Brochhausen
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, United States
| | | | - Sam Habiel
- Open Source Electronic Health Record Alliance, Washington, DC, United States
| | | | - Maria Herrero-Zazo
- The European Bioinformatics Institute, Birney Research Group, London, United Kingdom
| | - Brian Hocum
- Genelex Corporation, Seattle, WA, United States
| | - John Horn
- School of Pharmacy, University of Washington, Seattle, WA, United States
| | - Brian LeBaron
- Southeast Louisiana Veterans Health Care System, New Orleans, LA, United States
| | - Daniel C. Malone
- Department of Pharmacotherapy, University of Utah, Salt Lake City, UT, United States
| | - Øystein Nytrø
- Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Thomas Reese
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, United States
| | - Katrina Romagnoli
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, United States
| | - Jodi Schneider
- School of Information Science, University of Illinois, Champaign, IL, United States
| | - Louisa (Yu) Zhang
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, United States
| | - Richard D. Boyce
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, United States
- Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, United States
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28
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Kanza S, Graham Frey J. Semantic Technologies in Drug Discovery. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11520-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022] Open
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29
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Giblin KA, Basili D, Afzal AM, Rosenbrier-Ribeiro L, Greene N, Barrett I, Hughes SJ, Bender A. New Associations between Drug-Induced Adverse Events in Animal Models and Humans Reveal Novel Candidate Safety Targets. Chem Res Toxicol 2020; 34:438-451. [PMID: 33338378 DOI: 10.1021/acs.chemrestox.0c00311] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
To improve our ability to extrapolate preclinical toxicity to humans, there is a need to understand and quantify the concordance of adverse events (AEs) between animal models and clinical studies. In the present work, we discovered 3011 statistically significant associations between preclinical and clinical AEs caused by drugs reported in the PharmaPendium database of which 2952 were new associations between toxicities encoded by different Medical Dictionary for Regulatory Activities terms across species. To find plausible and testable candidate off-target drug activities for the derived associations, we investigated the genetic overlap between the genes linked to both a preclinical and a clinical AE and the protein targets found to interact with one or more drugs causing both AEs. We discuss three associations from the analysis in more detail for which novel candidate off-target drug activities could be identified, namely, the association of preclinical mutagenicity readouts with clinical teratospermia and ovarian failure, the association of preclinical reflexes abnormal with clinical poor-quality sleep, and the association of preclinical psychomotor hyperactivity with clinical drug withdrawal syndrome. Our analysis successfully identified a total of 77% of known safety targets currently tested in in vitro screening panels plus an additional 431 genes which were proposed for investigation as future safety targets for different clinical toxicities. This work provides new translational toxicity relationships beyond AE term-matching, the results of which can be used for risk profiling of future new chemical entities for clinical studies and for the development of future in vitro safety panels.
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Affiliation(s)
- Kathryn A Giblin
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom.,Medicinal Chemistry, Research and Early Development, Oncology R&D, AstraZeneca, Cambridge CB4 0WG, United Kingdom
| | - Danilo Basili
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom
| | - Avid M Afzal
- Data Sciences and Quantitative Biology, Discovery Sciences, R&D, AstraZeneca, Cambridge CB4 0WG, United Kingdom
| | - Lyn Rosenbrier-Ribeiro
- Safety Platforms, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge CB4 0WG, United Kingdom
| | - Nigel Greene
- Data Science and Artificial Intelligence, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Boston, Massachusetts 02451, United States
| | - Ian Barrett
- Data Sciences and Quantitative Biology, Discovery Sciences, R&D, AstraZeneca, Cambridge CB4 0WG, United Kingdom
| | - Samantha J Hughes
- Medicinal Chemistry, Research and Early Development, Oncology R&D, AstraZeneca, Cambridge CB4 0WG, United Kingdom
| | - Andreas Bender
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom
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Slater LT, Gkoutos GV, Hoehndorf R. Towards semantic interoperability: finding and repairing hidden contradictions in biomedical ontologies. BMC Med Inform Decis Mak 2020; 20:311. [PMID: 33319712 PMCID: PMC7736131 DOI: 10.1186/s12911-020-01336-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Accepted: 11/16/2020] [Indexed: 12/25/2022] Open
Abstract
Background Ontologies are widely used throughout the biomedical domain. These ontologies formally represent the classes and relations assumed to exist within a domain. As scientific domains are deeply interlinked, so too are their representations. While individual ontologies can be tested for consistency and coherency using automated reasoning methods, systematically combining ontologies of multiple domains together may reveal previously hidden contradictions. Methods We developed a method that tests for hidden unsatisfiabilities in an ontology that arise when combined with other ontologies. For this purpose, we combined sets of ontologies and use automated reasoning to determine whether unsatisfiable classes are present. In addition, we designed and implemented a novel algorithm that can determine justifications for contradictions across extremely large and complicated ontologies, and use these justifications to semi-automatically repair ontologies by identifying a small set of axioms that, when removed, result in a consistent and coherent set of ontologies.
Results We tested the mutual consistency of the OBO Foundry and the OBO ontologies and find that the combined OBO Foundry gives rise to at least 636 unsatisfiable classes, while the OBO ontologies give rise to more than 300,000 unsatisfiable classes. We also applied our semi-automatic repair algorithm to each combination of OBO ontologies that resulted in unsatisfiable classes, finding that only 117 axioms could be removed to account for all cases of unsatisfiability across all OBO ontologies. Conclusions We identified a large set of hidden unsatisfiability across a broad range of biomedical ontologies, and we find that this large set of unsatisfiable classes is the result of a relatively small amount of axiomatic disagreements. Our results show that hidden unsatisfiability is a serious problem in ontology interoperability; however, our results also provide a way towards more consistent ontologies by addressing the issues we identified.
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Affiliation(s)
- Luke T Slater
- College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, B15 2TT, UK. .,Institute of Translational Medicine, University Hospitals Birmingham, NHS Foundation Trust, Birmingham, B15 2TT, UK.
| | - Georgios V Gkoutos
- College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, B15 2TT, UK.,Institute of Translational Medicine, University Hospitals Birmingham, NHS Foundation Trust, Birmingham, B15 2TT, UK.,NIHR Experimental Cancer Medicine Centre, Birmingham, B15 2TT, UK.,NIHR Surgical Reconstruction and Microbiology Research Centre, Birmingham, B15 2TT, UK.,NIHR Biomedical Research Centre, Birmingham, B15 2TT, UK.,MRC Health Data Research UK (HDR UK Midlands, Birmingham, B15 2TT, UK
| | - Robert Hoehndorf
- Computer, Electrical and Mathematical Sciences and Engineering Division, Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal, 23955, Saudi Arabia
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Li X, Lin X, Ren H, Guo J. Ontological Organization and Bioinformatic Analysis of Adverse Drug Reactions From Package Inserts: Development and Usability Study. J Med Internet Res 2020; 22:e20443. [PMID: 32706718 PMCID: PMC7400033 DOI: 10.2196/20443] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Revised: 06/11/2020] [Accepted: 06/14/2020] [Indexed: 01/27/2023] Open
Abstract
BACKGROUND Licensed drugs may cause unexpected adverse reactions in patients, resulting in morbidity, risk of mortality, therapy disruptions, and prolonged hospital stays. Officially approved drug package inserts list the adverse reactions identified from randomized controlled clinical trials with high evidence levels and worldwide postmarketing surveillance. Formal representation of the adverse drug reaction (ADR) enclosed in semistructured package inserts will enable deep recognition of side effects and rational drug use, substantially reduce morbidity, and decrease societal costs. OBJECTIVE This paper aims to present an ontological organization of traceable ADR information extracted from licensed package inserts. In addition, it will provide machine-understandable knowledge for bioinformatics analysis, semantic retrieval, and intelligent clinical applications. METHODS Based on the essential content of package inserts, a generic ADR ontology model is proposed from two dimensions (and nine subdimensions), covering the ADR information and medication instructions. This is followed by a customized natural language processing method programmed with Python to retrieve the relevant information enclosed in package inserts. After the biocuration and identification of retrieved data from the package insert, an ADR ontology is automatically built for further bioinformatic analysis. RESULTS We collected 165 package inserts of quinolone drugs from the National Medical Products Administration and other drug databases in China, and built a specialized ADR ontology containing 2879 classes and 15,711 semantic relations. For each quinolone drug, the reported ADR information and medication instructions have been logically represented and formally organized in an ADR ontology. To demonstrate its usage, the source data were further bioinformatically analyzed. For example, the number of drug-ADR triples and major ADRs associated with each active ingredient were recorded. The 10 ADRs most frequently observed among quinolones were identified and categorized based on the 18 categories defined in the proposal. The occurrence frequency, severity, and ADR mitigation method explicitly stated in package inserts were also analyzed, as well as the top 5 specific populations with contraindications for quinolone drugs. CONCLUSIONS Ontological representation and organization using officially approved information from drug package inserts enables the identification and bioinformatic analysis of adverse reactions caused by a specific drug with regard to predefined ADR ontology classes and semantic relations. The resulting ontology-based ADR knowledge source classifies drug-specific adverse reactions, and supports a better understanding of ADRs and safer prescription of medications.
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Affiliation(s)
- Xiaoying Li
- Institute of Medical Information, Chinese Academy of Medical Sciences, Beijing, China
| | - Xin Lin
- Institute of Medical Information, Chinese Academy of Medical Sciences, Beijing, China
| | - Huiling Ren
- Institute of Medical Information, Chinese Academy of Medical Sciences, Beijing, China
| | - Jinjing Guo
- Institute of Medical Information, Chinese Academy of Medical Sciences, Beijing, China
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Oliveira D, Butt AS, Haller A, Rebholz-Schuhmann D, Sahay R. Where to search top-K biomedical ontologies? Brief Bioinform 2020; 20:1477-1491. [PMID: 29579141 PMCID: PMC6781604 DOI: 10.1093/bib/bby015] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2017] [Revised: 02/12/2018] [Indexed: 01/08/2023] Open
Abstract
Motivation Searching for precise terms and terminological definitions in the biomedical data space is problematic, as researchers find overlapping, closely related and even equivalent concepts in a single or multiple ontologies. Search engines that retrieve ontological resources often suggest an extensive list of search results for a given input term, which leads to the tedious task of selecting the best-fit ontological resource (class or property) for the input term and reduces user confidence in the retrieval engines. A systematic evaluation of these search engines is necessary to understand their strengths and weaknesses in different search requirements. Result We have implemented seven comparable Information Retrieval ranking algorithms to search through ontologies and compared them against four search engines for ontologies. Free-text queries have been performed, the outcomes have been judged by experts and the ranking algorithms and search engines have been evaluated against the expert-based ground truth (GT). In addition, we propose a probabilistic GT that is developed automatically to provide deeper insights and confidence to the expert-based GT as well as evaluating a broader range of search queries. Conclusion The main outcome of this work is the identification of key search factors for biomedical ontologies together with search requirements and a set of recommendations that will help biomedical experts and ontology engineers to select the best-suited retrieval mechanism in their search scenarios. We expect that this evaluation will allow researchers and practitioners to apply the current search techniques more reliably and that it will help them to select the right solution for their daily work. Availability The source code (of seven ranking algorithms), ground truths and experimental results are available at https://github.com/danielapoliveira/bioont-search-benchmark
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Affiliation(s)
| | | | - Armin Haller
- Australian National University, Canberra, Australia
| | | | - Ratnesh Sahay
- Insight Centre for Data Analytics, NUI Galway, Ireland
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Yang Y, Wybrow M, Li YF, Czauderna T, He Y. OntoPlot: A Novel Visualisation for Non-hierarchical Associations in Large Ontologies. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2020; 26:1140-1150. [PMID: 31442991 DOI: 10.1109/tvcg.2019.2934557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Ontologies are formal representations of concepts and complex relationships among them. They have been widely used to capture comprehensive domain knowledge in areas such as biology and medicine, where large and complex ontologies can contain hundreds of thousands of concepts. Especially due to the large size of ontologies, visualisation is useful for authoring, exploring and understanding their underlying data. Existing ontology visualisation tools generally focus on the hierarchical structure, giving much less emphasis to non-hierarchical associations. In this paper we present OntoPlot, a novel visualisation specifically designed to facilitate the exploration of all concept associations whilst still showing an ontology's large hierarchical structure. This hybrid visualisation combines icicle plots, visual compression techniques and interactivity, improving space-efficiency and reducing visual structural complexity. We conducted a user study with domain experts to evaluate the usability of OntoPlot, comparing it with the de facto ontology editor Protégé. The results confirm that OntoPlot attains our design goals for association-related tasks and is strongly favoured by domain experts.
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Abstract
This Editorial first introduces the background of the vaccine and drug relations and how biomedical terminologies and ontologies have been used to support their studies. The history of the seven workshops, initially named VDOSME, and then named VDOS, is also summarized and introduced. Then the 7th International Workshop on Vaccine and Drug Ontology Studies (VDOS 2018), held on August 10th, 2018, Corvallis, Oregon, USA, is introduced in detail. These VDOS workshops have greatly supported the development, applications, and discussion of vaccine- and drug-related terminology and drug studies.
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Affiliation(s)
- Junguk Hur
- Department of Biomedical Sciences, University of North Dakota School of Medicine and Health Sciences, Grand Forks, ND USA
| | - Cui Tao
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX USA
| | - Yongqun He
- Unit for Laboratory Animal Medicine, Department of Microbiology and Immunology, Center for Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI USA
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Tiftikci M, Özgür A, He Y, Hur J. Machine learning-based identification and rule-based normalization of adverse drug reactions in drug labels. BMC Bioinformatics 2019; 20:707. [PMID: 31865904 PMCID: PMC6927101 DOI: 10.1186/s12859-019-3195-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Background Use of medication can cause adverse drug reactions (ADRs), unwanted or unexpected events, which are a major safety concern. Drug labels, or prescribing information or package inserts, describe ADRs. Therefore, systematically identifying ADR information from drug labels is critical in multiple aspects; however, this task is challenging due to the nature of the natural language of drug labels. Results In this paper, we present a machine learning- and rule-based system for the identification of ADR entity mentions in the text of drug labels and their normalization through the Medical Dictionary for Regulatory Activities (MedDRA) dictionary. The machine learning approach is based on a recently proposed deep learning architecture, which integrates bi-directional Long Short-Term Memory (Bi-LSTM), Convolutional Neural Network (CNN), and Conditional Random Fields (CRF) for entity recognition. The rule-based approach, used for normalizing the identified ADR mentions to MedDRA terms, is based on an extension of our in-house text-mining system, SciMiner. We evaluated our system on the Text Analysis Conference (TAC) Adverse Drug Reaction 2017 challenge test data set, consisting of 200 manually curated US FDA drug labels. Our ML-based system achieved 77.0% F1 score on the task of ADR mention recognition and 82.6% micro-averaged F1 score on the task of ADR normalization, while rule-based system achieved 67.4 and 77.6% F1 scores, respectively. Conclusion Our study demonstrates that a system composed of a deep learning architecture for entity recognition and a rule-based model for entity normalization is a promising approach for ADR extraction from drug labels.
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Affiliation(s)
- Mert Tiftikci
- Department of Computer Engineering, Boğaziçi University, İstanbul, 34342, Turkey
| | - Arzucan Özgür
- Department of Computer Engineering, Boğaziçi University, İstanbul, 34342, Turkey
| | - Yongqun He
- Unit for Laboratory Animal Medicine, Department of Microbiology and Immunology, Center for Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, 48109, MI, USA
| | - Junguk Hur
- Department of Biomedical Sciences, University of North Dakota School of Medicine and Health Sciences, 1301 North Columbia Rd, Grand Forks, North Dakota, 58202, USA.
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Bousquet C, Souvignet J, Sadou É, Jaulent MC, Declerck G. Ontological and Non-Ontological Resources for Associating Medical Dictionary for Regulatory Activities Terms to SNOMED Clinical Terms With Semantic Properties. Front Pharmacol 2019; 10:975. [PMID: 31551780 PMCID: PMC6747929 DOI: 10.3389/fphar.2019.00975] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2018] [Accepted: 07/31/2019] [Indexed: 11/20/2022] Open
Abstract
Background: Formal definitions allow selecting terms (e.g., identifying all terms related to “Infectious disease” using the query “has causative agent organism”) and terminological reasoning (e.g., “hepatitis B” is a “hepatitis” and is an “infectious disease”). However, the standard international terminology Medical Dictionary for Regulatory Activities (MedDRA) used for coding adverse drug reactions in pharmacovigilance databases does not beneficiate from such formal definitions. Our objective was to evaluate the potential of reuse of ontological and non-ontological resources for generating such definitions for MedDRA. Methods: We developed several methods that collectively allow a semiautomatic semantic enrichment of MedDRA: 1) using MedDRA-to-SNOMED Clinical Terms (SNOMED CT) mappings (available in the Unified Medical Language System metathesaurus or other mapping resources, e.g., the MedDRA preferred term “hepatitis B” is associated to the SNOMED CT concept “type B viral hepatitis”) to extract term definitions (e.g., “hepatitis B” is associated with the following properties: has finding site liver structure, has associated morphology inflammation morphology, and has causative agent hepatitis B virus); 2) using MedDRA labels and lexical/syntactic methods for automatic decomposition of complex MedDRA terms (e.g., the MedDRA systems organ class “blood and lymphatic system disorders” is decomposed in blood system disorders and lymphatic system disorders) or automatic suggestions of properties (e.g., the string “cyclic” in preferred term “cyclic neutropenia” leads to the property has clinical course cyclic). Results: The Unified Medical Language System metathesaurus was the main ontological resource reusable for generating formal definitions for MedDRA terms. The non-ontological resources (another mapping resource provided by Nadkarni and Darer in 2010 and MedDRA labels) allowed defining few additional preferred terms. While the Ci4SeR tool helped the curator to define 1,935 terms by suggesting potential supplemental relations based on the parents’ and siblings’ semantic definition, defining manually all MedDRA terms remains expensive in time. Discussion: Several ontological and non-ontological resources are available for associating MedDRA terms to SNOMED CT concepts with semantic properties, but providing manual definitions is still necessary. The ontology of adverse events is a possible alternative but does not cover all MedDRA terms either. Perspectives are to implement more efficient techniques to find more logical relations between SNOMED CT and MedDRA in an automated way.
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Affiliation(s)
- Cédric Bousquet
- Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances en e-Santé, LIMICS, Sorbonne Université, Inserm, Université Paris 13, Paris, France.,Unit of Public Health and Medical Informatics, University of Saint Etienne, Saint Etienne, France
| | - Julien Souvignet
- Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances en e-Santé, LIMICS, Sorbonne Université, Inserm, Université Paris 13, Paris, France.,Unit of Public Health and Medical Informatics, University of Saint Etienne, Saint Etienne, France
| | - Éric Sadou
- Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances en e-Santé, LIMICS, Sorbonne Université, Inserm, Université Paris 13, Paris, France
| | - Marie-Christine Jaulent
- Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances en e-Santé, LIMICS, Sorbonne Université, Inserm, Université Paris 13, Paris, France
| | - Gunnar Declerck
- EA 2223 Costech (Connaissance, Organisation et Systèmes Techniques), Centre de Recherche, Sorbonne Universités, Université de technologie de Compiègne, Compiègne, France
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Natsiavas P, Malousi A, Bousquet C, Jaulent MC, Koutkias V. Computational Advances in Drug Safety: Systematic and Mapping Review of Knowledge Engineering Based Approaches. Front Pharmacol 2019; 10:415. [PMID: 31156424 PMCID: PMC6533857 DOI: 10.3389/fphar.2019.00415] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2018] [Accepted: 04/02/2019] [Indexed: 12/12/2022] Open
Abstract
Drug Safety (DS) is a domain with significant public health and social impact. Knowledge Engineering (KE) is the Computer Science discipline elaborating on methods and tools for developing “knowledge-intensive” systems, depending on a conceptual “knowledge” schema and some kind of “reasoning” process. The present systematic and mapping review aims to investigate KE-based approaches employed for DS and highlight the introduced added value as well as trends and possible gaps in the domain. Journal articles published between 2006 and 2017 were retrieved from PubMed/MEDLINE and Web of Science® (873 in total) and filtered based on a comprehensive set of inclusion/exclusion criteria. The 80 finally selected articles were reviewed on full-text, while the mapping process relied on a set of concrete criteria (concerning specific KE and DS core activities, special DS topics, employed data sources, reference ontologies/terminologies, and computational methods, etc.). The analysis results are publicly available as online interactive analytics graphs. The review clearly depicted increased use of KE approaches for DS. The collected data illustrate the use of KE for various DS aspects, such as Adverse Drug Event (ADE) information collection, detection, and assessment. Moreover, the quantified analysis of using KE for the respective DS core activities highlighted room for intensifying research on KE for ADE monitoring, prevention and reporting. Finally, the assessed use of the various data sources for DS special topics demonstrated extensive use of dominant data sources for DS surveillance, i.e., Spontaneous Reporting Systems, but also increasing interest in the use of emerging data sources, e.g., observational healthcare databases, biochemical/genetic databases, and social media. Various exemplar applications were identified with promising results, e.g., improvement in Adverse Drug Reaction (ADR) prediction, detection of drug interactions, and novel ADE profiles related with specific mechanisms of action, etc. Nevertheless, since the reviewed studies mostly concerned proof-of-concept implementations, more intense research is required to increase the maturity level that is necessary for KE approaches to reach routine DS practice. In conclusion, we argue that efficiently addressing DS data analytics and management challenges requires the introduction of high-throughput KE-based methods for effective knowledge discovery and management, resulting ultimately, in the establishment of a continuous learning DS system.
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Affiliation(s)
- Pantelis Natsiavas
- Institute of Applied Biosciences, Centre for Research and Technology Hellas, Thessaloniki, Greece.,Sorbonne Université, INSERM, Univ Paris 13, Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances pour la e-Santé, LIMICS, Paris, France
| | - Andigoni Malousi
- Laboratory of Biological Chemistry, Department of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Cédric Bousquet
- Sorbonne Université, INSERM, Univ Paris 13, Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances pour la e-Santé, LIMICS, Paris, France.,Public Health and Medical Information Unit, University Hospital of Saint-Etienne, Saint-Étienne, France
| | - Marie-Christine Jaulent
- Sorbonne Université, INSERM, Univ Paris 13, Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances pour la e-Santé, LIMICS, Paris, France
| | - Vassilis Koutkias
- Institute of Applied Biosciences, Centre for Research and Technology Hellas, Thessaloniki, Greece
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Yu H, Nysak S, Garg N, Ong E, Ye X, Zhang X, He Y. ODAE: Ontology-based systematic representation and analysis of drug adverse events and its usage in study of adverse events given different patient age and disease conditions. BMC Bioinformatics 2019; 20:199. [PMID: 31074377 PMCID: PMC6509876 DOI: 10.1186/s12859-019-2729-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/04/2022] Open
Abstract
Background Drug adverse events (AEs), or called adverse drug events (ADEs), are ranked one of the leading causes of mortality. The Ontology of Adverse Events (OAE) has been widely used for adverse event AE representation, standardization, and analysis. OAE-based ADE-specific ontologies, including ODNAE for drug-associated neuropathy-inducing AEs and OCVDAE for cardiovascular drug AEs, have also been developed and used. However, these ADE-specific ontologies do not consider the effects of other factors (e.g., age and drug-treated disease) on the outcomes of ADEs. With more ontological studies of ADEs, it is also critical to develop a general purpose ontology for representing ADEs for various types of drugs. Results Our survey of FDA drug package insert documents and other resources for 224 neuropathy-inducing drugs discovered that many drugs (e.g., sirolimus and linezolid) cause different AEs given patients’ age or the diseases treated by the drugs. To logically represent the complex relations among drug, drug ingredient and mechanism of action, AE, age, disease, and other related factors, an ontology design pattern was developed and applied to generate a community-driven open-source Ontology of Drug Adverse Events (ODAE). The ODAE development follows the OBO Foundry ontology development principles (e.g., openness and collaboration). Built on a generalizable ODAE design pattern and extending the OAE and NDF-RT ontology, ODAE has represented various AEs associated with the over 200 neuropathy-inducing drugs given different age and disease conditions. ODAE is now deposited in the Ontobee for browsing and queries. As a demonstration of usage, a SPARQL query of the ODAE knowledge base was developed to identify all the drugs having the mechanisms of ion channel interactions, the diseases treated with the drugs, and AEs after the treatment in adult patients. AE-specific drug class effects were also explored using ODAE and SPARQL. Conclusion ODAE provides a general representation of ADEs given different conditions and can be used for querying scientific questions. ODAE is also a robust knowledge base and platform for semantic and logic representation and study of ADEs of more drugs in the future. Electronic supplementary material The online version of this article (10.1186/s12859-019-2729-1) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Hong Yu
- Department of Pulmonary and Critical Care Medicine, Guizhou Provincial People's Hospital, Guiyang, 550002, Guizhou, China. .,Guizhou University Medical College, Guiyang, 550025, Guizhou, China.
| | - Solomiya Nysak
- College of Literature, Science, and the Arts, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Noemi Garg
- College of Pharmacy, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Edison Ong
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
| | - Xianwei Ye
- Department of Pulmonary and Critical Care Medicine, Guizhou Provincial People's Hospital, Guiyang, 550002, Guizhou, China.,Guizhou University Medical College, Guiyang, 550025, Guizhou, China
| | - Xiangyan Zhang
- Department of Pulmonary and Critical Care Medicine, Guizhou Provincial People's Hospital, Guiyang, 550002, Guizhou, China.,Guizhou University Medical College, Guiyang, 550025, Guizhou, China
| | - Yongqun He
- Unit for Laboratory Animal Medicine, Department of Microbiology and Immunology, Center for Computational Medicine and Bioinformatics, and Comprehensive Cancer Center, University of Michigan Medical School, Ann Arbor, MI, 48109, USA.
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Wilson JL, Racz R, Liu T, Adeniyi O, Sun J, Ramamoorthy A, Pacanowski M, Altman R. PathFX provides mechanistic insights into drug efficacy and safety for regulatory review and therapeutic development. PLoS Comput Biol 2018; 14:e1006614. [PMID: 30532240 PMCID: PMC6285459 DOI: 10.1371/journal.pcbi.1006614] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2018] [Accepted: 10/31/2018] [Indexed: 12/14/2022] Open
Abstract
Failure to demonstrate efficacy and safety issues are important reasons that drugs do not reach the market. An incomplete understanding of how drugs exert their effects hinders regulatory and pharmaceutical industry projections of a drug's benefits and risks. Signaling pathways mediate drug response and while many signaling molecules have been characterized for their contribution to disease or their role in drug side effects, our knowledge of these pathways is incomplete. To better understand all signaling molecules involved in drug response and the phenotype associations of these molecules, we created a novel method, PathFX, a non-commercial entity, to identify these pathways and drug-related phenotypes. We benchmarked PathFX by identifying drugs' marketed disease indications and reported a sensitivity of 41%, a 2.7-fold improvement over similar approaches. We then used PathFX to strengthen signals for drug-adverse event pairs occurring in the FDA Adverse Event Reporting System (FAERS) and also identified opportunities for drug repurposing for new diseases based on interaction paths that associated a marketed drug to that disease. By discovering molecular interaction pathways, PathFX improved our understanding of drug associations to safety and efficacy phenotypes. The algorithm may provide a new means to improve regulatory and therapeutic development decisions.
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Affiliation(s)
- Jennifer L. Wilson
- Department of Bioengineering, Stanford University, Palo Alto California, United States of America
| | - Rebecca Racz
- Division of Applied Regulatory Science, US Food and Drug Administration, Silver Spring Maryland, United States of America
| | - Tianyun Liu
- Department of Bioengineering, Stanford University, Palo Alto California, United States of America
| | - Oluseyi Adeniyi
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring Maryland, United States of America
| | - Jielin Sun
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring Maryland, United States of America
| | - Anuradha Ramamoorthy
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring Maryland, United States of America
| | - Michael Pacanowski
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring Maryland, United States of America
| | - Russ Altman
- Department of Bioengineering, Stanford University, Palo Alto California, United States of America
- Department of Genetics, Stanford University, Palo Alto California, United States of America
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Gkoutos GV, Schofield PN, Hoehndorf R. The anatomy of phenotype ontologies: principles, properties and applications. Brief Bioinform 2018; 19:1008-1021. [PMID: 28387809 PMCID: PMC6169674 DOI: 10.1093/bib/bbx035] [Citation(s) in RCA: 48] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2017] [Revised: 02/05/2017] [Indexed: 12/14/2022] Open
Abstract
The past decade has seen an explosion in the collection of genotype data in domains as diverse as medicine, ecology, livestock and plant breeding. Along with this comes the challenge of dealing with the related phenotype data, which is not only large but also highly multidimensional. Computational analysis of phenotypes has therefore become critical for our ability to understand the biological meaning of genomic data in the biological sciences. At the heart of computational phenotype analysis are the phenotype ontologies. A large number of these ontologies have been developed across many domains, and we are now at a point where the knowledge captured in the structure of these ontologies can be used for the integration and analysis of large interrelated data sets. The Phenotype And Trait Ontology framework provides a method for formal definitions of phenotypes and associated data sets and has proved to be key to our ability to develop methods for the integration and analysis of phenotype data. Here, we describe the development and products of the ontological approach to phenotype capture, the formal content of phenotype ontologies and how their content can be used computationally.
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Affiliation(s)
| | | | - Robert Hoehndorf
- Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, King Abdullah University of Science and Technology, Thuwal
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Disease Specific Ontology of Adverse Events: Ontology extension and adaptation for Chronic Kidney Disease. Comput Biol Med 2018; 101:210-217. [PMID: 30195820 DOI: 10.1016/j.compbiomed.2018.08.024] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2018] [Revised: 08/22/2018] [Accepted: 08/22/2018] [Indexed: 11/21/2022]
Abstract
BACKGROUND Adverse Event (AE) ontology can be used to support interoperability and computer-assisted reasoning of AEs. Despite significant progress in developing biomedical ontologies, they are facing the obstacle of adoption partly because those ontologies are too general to meet the requirements of a specific domain. Understanding and representing of AEs for a specific domain such as Chronic Kidney Disease (CKD) has both theoretical and clinical significance. CKD patients are at a high risk for an array of disease-intervention specific AEs, and these in turn can contribute to disease progression unlike other diseases. This study proposes Disease Specific Ontology of Adverse Events (DSOAE) to address specific requirements of CKD, and applies it to different usage scenarios with real data. METHODS We introduce a method for developing DSOAE through the extension and adaption of general ontologies by incorporating domain-specific information and usage requirements. It starts with specifying the goal and scope of a target domain (i.e. selecting seed ontologies), followed by identifying main AE classes and relations, extracting and creating classes and relations, aligning and identifying upper-level classes and lower-level classes, and finally populating the ontology with instances. Any of these steps may be repeated to refine the ontology. RESULTS DSOAE contains 22 CKD-specific AE classes, which are grouped into two general categories: patient-reported AEs and biochemical/laboratory-related AEs. In addition, disease history and comorbidity classes as introduced in this study help model patient-related risk factors for AEs. With the support of DSOAE, we build a knowledge base of CKD-specific AEs using data from different sources (e.g. patient cohort data and social media), and apply the knowledge base to data analysis and data integration. CONCLUSIONS DSOAE enables the interoperability of AEs across different sources and supports the development of a knowledge base of domain-specific AEs. DSOAE can also meet the needs of different usage scenarios. The approach to constructing DSOAE is generalizable and can be used to develop AE ontology in other domains.
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Natsiavas P, Boyce RD, Jaulent MC, Koutkias V. OpenPVSignal: Advancing Information Search, Sharing and Reuse on Pharmacovigilance Signals via FAIR Principles and Semantic Web Technologies. Front Pharmacol 2018; 9:609. [PMID: 29997499 PMCID: PMC6028717 DOI: 10.3389/fphar.2018.00609] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2018] [Accepted: 05/21/2018] [Indexed: 12/27/2022] Open
Abstract
Signal detection and management is a key activity in pharmacovigilance (PV). When a new PV signal is identified, the respective information is publicly communicated in the form of periodic newsletters or reports by organizations that monitor and investigate PV-related information (such as the World Health Organization and national PV centers). However, this type of communication does not allow for systematic access, discovery and explicit data interlinking and, therefore, does not facilitate automated data sharing and reuse. In this paper, we present OpenPVSignal, a novel ontology aiming to support the semantic enrichment and rigorous communication of PV signal information in a systematic way, focusing on two key aspects: (a) publishing signal information according to the FAIR (Findable, Accessible, Interoperable, and Re-usable) data principles, and (b) exploiting automatic reasoning capabilities upon the interlinked PV signal report data. OpenPVSignal is developed as a reusable, extendable and machine-understandable model based on Semantic Web standards/recommendations. In particular, it can be used to model PV signal report data focusing on: (a) heterogeneous data interlinking, (b) semantic and syntactic interoperability, (c) provenance tracking and (d) knowledge expressiveness. OpenPVSignal is built upon widely-accepted semantic models, namely, the provenance ontology (PROV-O), the Micropublications semantic model, the Web Annotation Data Model (WADM), the Ontology of Adverse Events (OAE) and the Time ontology. To this end, we describe the design of OpenPVSignal and demonstrate its applicability as well as the reasoning capabilities enabled by its use. We also provide an evaluation of the model against the FAIR data principles. The applicability of OpenPVSignal is demonstrated by using PV signal information published in: (a) the World Health Organization's Pharmaceuticals Newsletter, (b) the Netherlands Pharmacovigilance Centre Lareb Web site and (c) the U.S. Food and Drug Administration (FDA) Drug Safety Communications, also available on the FDA Web site.
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Affiliation(s)
- Pantelis Natsiavas
- Centre for Research & Technology Hellas, Institute of Applied Biosciences, Thessaloniki, Greece.,Lab of Computing, Medical Informatics & Biomedical Imaging Technologies, Department of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Richard D Boyce
- Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
| | - Marie-Christine Jaulent
- Institut National de la Santé et de la Recherche Médicale, U1142, LIMICS, Paris, France.,Sorbonne Universités, UPMC Univ Paris 06, UMR_S 1142, LIMICS, Paris, France.,Université Paris 13, Sorbonne Paris Cité, UMR_S 1142, LIMICS, Villetaneuse, France
| | - Vassilis Koutkias
- Centre for Research & Technology Hellas, Institute of Applied Biosciences, Thessaloniki, Greece.,Lab of Computing, Medical Informatics & Biomedical Imaging Technologies, Department of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
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Hur J, Özgür A, He Y. Ontology-based literature mining and class effect analysis of adverse drug reactions associated with neuropathy-inducing drugs. J Biomed Semantics 2018; 9:17. [PMID: 29880031 PMCID: PMC5991464 DOI: 10.1186/s13326-018-0185-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2018] [Accepted: 05/18/2018] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND Adverse drug reactions (ADRs), also called as drug adverse events (AEs), are reported in the FDA drug labels; however, it is a big challenge to properly retrieve and analyze the ADRs and their potential relationships from textual data. Previously, we identified and ontologically modeled over 240 drugs that can induce peripheral neuropathy through mining public drug-related databases and drug labels. However, the ADR mechanisms of these drugs are still unclear. In this study, we aimed to develop an ontology-based literature mining system to identify ADRs from drug labels and to elucidate potential mechanisms of the neuropathy-inducing drugs (NIDs). RESULTS We developed and applied an ontology-based SciMiner literature mining strategy to mine ADRs from the drug labels provided in the Text Analysis Conference (TAC) 2017, which included drug labels for 53 neuropathy-inducing drugs (NIDs). We identified an average of 243 ADRs per NID and constructed an ADR-ADR network, which consists of 29 ADR nodes and 149 edges, including only those ADR-ADR pairs found in at least 50% of NIDs. Comparison to the ADR-ADR network of non-NIDs revealed that the ADRs such as pruritus, pyrexia, thrombocytopenia, nervousness, asthenia, acute lymphocytic leukaemia were highly enriched in the NID network. Our ChEBI-based ontology analysis identified three benzimidazole NIDs (i.e., lansoprazole, omeprazole, and pantoprazole), which were associated with 43 ADRs. Based on ontology-based drug class effect definition, the benzimidazole drug group has a drug class effect on all of these 43 ADRs. Many of these 43 ADRs also exist in the enriched NID ADR network. Our Ontology of Adverse Events (OAE) classification further found that these 43 benzimidazole-related ADRs were distributed in many systems, primarily in behavioral and neurological, digestive, skin, and immune systems. CONCLUSIONS Our study demonstrates that ontology-based literature mining and network analysis can efficiently identify and study specific group of drugs and their associated ADRs. Furthermore, our analysis of drug class effects identified 3 benzimidazole drugs sharing 43 ADRs, leading to new hypothesis generation and possible mechanism understanding of drug-induced peripheral neuropathy.
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Affiliation(s)
- Junguk Hur
- Department of Department of Biomedical Sciences, University of North Dakota School of Medicine and Health Sciences, Grand Forks, ND, 58202, USA.
| | - Arzucan Özgür
- Department of Computer Engineering, Bogazici University, 34342, Istanbul, Turkey
| | - Yongqun He
- Unit for Laboratory Animal Medicine, Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA. .,Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA. .,Center for Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, 48109, USA. .,Comprehensive Cancer Center, University of Michigan Medical School, Ann Arbor, MI, 48109, USA.
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Xie J, Wang J, Li Z, Wang W, Pang Y, He Y. Ontology-Based Meta-Analysis of Animal and Human Adverse Events Associated With Licensed Brucellosis Vaccines. Front Pharmacol 2018; 9:503. [PMID: 29867505 PMCID: PMC5962797 DOI: 10.3389/fphar.2018.00503] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2017] [Accepted: 04/26/2018] [Indexed: 01/18/2023] Open
Abstract
Brucella abortus strain 19 (S19), Brucella melitensis Rev 1 (Rev1), and B. abortus strain RB51 (RB51) are the three licensed animal brucellosis vaccines, and they have been most commonly and successfully used in prevent brucellosis in animals. However, many adverse events (AEs) have been associated with these three vaccines after their administering to animals or being accidentally exposed to humans. In this study, 27 peer-reviewed publications containing animal and human AE reports associated with these three brucellosis vaccines were manually annotated from the PubMed database. Our meta-analysis identified 20 animal AEs and 46 human AEs associated with the three vaccines. Based on the Ontology of Adverse Events (OAE) hierarchical classification, these animal AEs were enriched in the immune and reproductive systems that might eventually result in the occurrence of abortion or infertility. The human AEs were concentrated in the behavioral and neurological conditions, and these AEs showed flu-like symptoms that are consistent with human brucellosis. Furthermore, an analysis of variance (ANOVA) statistics analysis with linear model fits was used to determine the major variables that might affect the occurrence of abortion AE in animals. The ANOVA results indicated that three variables (P-value < 0.05) are significantly associated with the occurrence of abortion AE: animal species, vaccination dose, and vaccination route. The other two variables (i.e., vaccine type and animal age at vaccination) did not significantly (P-value > 0.05) associated with the occurrence of abortion AE. Overall, this study represents the first ontology-based meta-analysis of adverse events associated with animal vaccines. The results of such a study led to the better understanding of brucellosis vaccine AEs, facilitating rational design of more secure and effective vaccines.
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Affiliation(s)
- Jiangan Xie
- School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing, China.,Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, United States
| | - Jessica Wang
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, United States
| | - Zhangyong Li
- School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Wei Wang
- School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Yu Pang
- School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Yongqun He
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, United States
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Wong MU, Racz R, Ong E, He Y. Towards precision informatics of pharmacovigilance: OAE-CTCAE mapping and OAE-based representation and analysis of adverse events in patients treated with cancer drugs. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2018; 2017:1793-1801. [PMID: 29854250 PMCID: PMC5977606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
A critical issue in the usage of cancer drugs is its association with various adverse events (AEs) in some, but not all, patients. The National Cancer Institute (NCI) Common Terminology Criteria for Adverse Events (CTCAE) is a controlled terminology for AE classification and analysis in cancer clinical trials. The Ontology of Adverse Events (OAE) is a community-based ontology in the domain of AEs. In this study, OAE was first updated by including AE severity grading and OAE-CTCAE mapping. An OAE subset containing CTCAE-related terms and their associated OAE terms was generated to facilitate term usage. A use case study based on a published cancer drug clinical trial demonstrates that OAE provides better hierarchical representation, includes semantic relations, and supports automated reasoning. Demonstrated with a single patient analysis, the OAE framework supports precision informatics for representing AEs and related genetic and clinical conditions in individual patients treated with cancer drugs.
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Affiliation(s)
- Mei U Wong
- University of Michigan, Ann Arbor, MI, USA
| | - Rebecca Racz
- University of Michigan, Ann Arbor, MI, USA
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Food and Drug Administration, Silver Spring, MD, USA
| | - Edison Ong
- University of Michigan, Ann Arbor, MI, USA
| | - Yongqun He
- University of Michigan, Ann Arbor, MI, USA
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He Y, Xiang Z, Zheng J, Lin Y, Overton JA, Ong E. The eXtensible ontology development (XOD) principles and tool implementation to support ontology interoperability. J Biomed Semantics 2018; 9:3. [PMID: 29329592 PMCID: PMC5765662 DOI: 10.1186/s13326-017-0169-2] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2017] [Accepted: 12/07/2017] [Indexed: 11/13/2022] Open
Abstract
Ontologies are critical to data/metadata and knowledge standardization, sharing, and analysis. With hundreds of biological and biomedical ontologies developed, it has become critical to ensure ontology interoperability and the usage of interoperable ontologies for standardized data representation and integration. The suite of web-based Ontoanimal tools (e.g., Ontofox, Ontorat, and Ontobee) support different aspects of extensible ontology development. By summarizing the common features of Ontoanimal and other similar tools, we identified and proposed an “eXtensible Ontology Development” (XOD) strategy and its associated four principles. These XOD principles reuse existing terms and semantic relations from reliable ontologies, develop and apply well-established ontology design patterns (ODPs), and involve community efforts to support new ontology development, promoting standardized and interoperable data and knowledge representation and integration. The adoption of the XOD strategy, together with robust XOD tool development, will greatly support ontology interoperability and robust ontology applications to support data to be Findable, Accessible, Interoperable and Reusable (i.e., FAIR).
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Affiliation(s)
- Yongqun He
- Unit for Laboratory Animal Medicine, Department of Microbiology and Immunology, Center for Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA.
| | - Zuoshuang Xiang
- Unit for Laboratory Animal Medicine, Department of Microbiology and Immunology, Center for Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Jie Zheng
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Yu Lin
- Center for Computational Science, University of Miami, Coral Gables, FL, USA
| | | | - Edison Ong
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA
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Zhu Y, Elemento O, Pathak J, Wang F. Drug knowledge bases and their applications in biomedical informatics research. Brief Bioinform 2018; 20:1308-1321. [DOI: 10.1093/bib/bbx169] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2017] [Revised: 11/15/2017] [Indexed: 11/14/2022] Open
Abstract
Abstract
Recent advances in biomedical research have generated a large volume of drug-related data. To effectively handle this flood of data, many initiatives have been taken to help researchers make good use of them. As the results of these initiatives, many drug knowledge bases have been constructed. They range from simple ones with specific focuses to comprehensive ones that contain information on almost every aspect of a drug. These curated drug knowledge bases have made significant contributions to the development of efficient and effective health information technologies for better health-care service delivery. Understanding and comparing existing drug knowledge bases and how they are applied in various biomedical studies will help us recognize the state of the art and design better knowledge bases in the future. In addition, researchers can get insights on novel applications of the drug knowledge bases through a review of successful use cases. In this study, we provide a review of existing popular drug knowledge bases and their applications in drug-related studies. We discuss challenges in constructing and using drug knowledge bases as well as future research directions toward a better ecosystem of drug knowledge bases.
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Garcia-Gathright JI, Matiasz NJ, Adame C, Sarma KV, Sauer L, Smedley NF, Spiegel ML, Strunck J, Garon EB, Taira RK, Aberle DR, Bui AAT. Evaluating Casama: Contextualized semantic maps for summarization of lung cancer studies. Comput Biol Med 2018; 92:55-63. [PMID: 29149658 PMCID: PMC5762403 DOI: 10.1016/j.compbiomed.2017.10.034] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2017] [Revised: 10/28/2017] [Accepted: 10/29/2017] [Indexed: 01/15/2023]
Abstract
OBJECTIVE It is crucial for clinicians to stay up to date on current literature in order to apply recent evidence to clinical decision making. Automatic summarization systems can help clinicians quickly view an aggregated summary of literature on a topic. Casama, a representation and summarization system based on "contextualized semantic maps," captures the findings of biomedical studies as well as the contexts associated with patient population and study design. This paper presents a user-oriented evaluation of Casama in comparison to a context-free representation, SemRep. MATERIALS AND METHODS The effectiveness of the representation was evaluated by presenting users with manually annotated Casama and SemRep summaries of ten articles on driver mutations in cancer. Automatic annotations were evaluated on a collection of articles on EGFR mutation in lung cancer. Seven users completed a questionnaire rating the summarization quality for various topics and applications. RESULTS Casama had higher median scores than SemRep for the majority of the topics (p≤ 0.00032), all of the applications (p≤ 0.00089), and in overall summarization quality (p≤ 1.5e-05). Casama's manual annotations outperformed Casama's automatic annotations (p = 0.00061). DISCUSSION Casama performed particularly well in the representation of strength of evidence, which was highly rated both quantitatively and qualitatively. Users noted that Casama's less granular, more targeted representation improved usability compared to SemRep. CONCLUSION This evaluation demonstrated the benefits of a contextualized representation for summarizing biomedical literature on cancer. Iteration on specific areas of Casama's representation, further development of its algorithms, and a clinically-oriented evaluation are warranted.
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Affiliation(s)
- Jean I Garcia-Gathright
- University of California, Los Angeles, Department of Bioengineering, 924 Westwood Boulevard, Suite 420, Los Angeles, CA, 90024, USA.
| | - Nicholas J Matiasz
- University of California, Los Angeles, Department of Bioengineering, 924 Westwood Boulevard, Suite 420, Los Angeles, CA, 90024, USA
| | - Carlos Adame
- University of California, Los Angeles, Department of Medicine - Division of Hematology-Oncology, 924 Westwood Boulevard, Suite 200, Los Angeles, CA, 90024, USA
| | - Karthik V Sarma
- University of California, Los Angeles, Department of Bioengineering, 924 Westwood Boulevard, Suite 420, Los Angeles, CA, 90024, USA
| | - Lauren Sauer
- University of California, Los Angeles, Department of Medicine - Division of Hematology-Oncology, 924 Westwood Boulevard, Suite 200, Los Angeles, CA, 90024, USA
| | - Nova F Smedley
- University of California, Los Angeles, Department of Bioengineering, 924 Westwood Boulevard, Suite 420, Los Angeles, CA, 90024, USA
| | - Marshall L Spiegel
- University of California, Los Angeles, Department of Medicine - Division of Hematology-Oncology, 924 Westwood Boulevard, Suite 200, Los Angeles, CA, 90024, USA
| | - Jennifer Strunck
- University of California, Los Angeles, Department of Medicine - Division of Hematology-Oncology, 924 Westwood Boulevard, Suite 200, Los Angeles, CA, 90024, USA
| | - Edward B Garon
- University of California, Los Angeles, Department of Medicine - Division of Hematology-Oncology, 924 Westwood Boulevard, Suite 200, Los Angeles, CA, 90024, USA
| | - Ricky K Taira
- University of California, Los Angeles, Department of Bioengineering, 924 Westwood Boulevard, Suite 420, Los Angeles, CA, 90024, USA; University of California, Los Angeles, Department of Radiological Sciences, 924 Westwood Boulevard, Suite 420, Los Angeles, CA, 90024, USA
| | - Denise R Aberle
- University of California, Los Angeles, Department of Bioengineering, 924 Westwood Boulevard, Suite 420, Los Angeles, CA, 90024, USA; University of California, Los Angeles, Department of Radiological Sciences, 924 Westwood Boulevard, Suite 420, Los Angeles, CA, 90024, USA
| | - Alex A T Bui
- University of California, Los Angeles, Department of Bioengineering, 924 Westwood Boulevard, Suite 420, Los Angeles, CA, 90024, USA; University of California, Los Angeles, Department of Radiological Sciences, 924 Westwood Boulevard, Suite 420, Los Angeles, CA, 90024, USA
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Ontology-based systematic representation and analysis of traditional Chinese drugs against rheumatism. BMC SYSTEMS BIOLOGY 2017; 11:130. [PMID: 29322929 PMCID: PMC5763303 DOI: 10.1186/s12918-017-0510-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Background Rheumatism represents any disease condition marked with inflammation and pain in the joints, muscles, or connective tissues. Many traditional Chinese drugs have been used for a long time to treat rheumatism. However, a comprehensive information source for these drugs is still missing, and their anti-rheumatism mechanisms remain unclear. An ontology for anti-rheumatism traditional Chinese drugs would strongly support the representation, analysis, and understanding of these drugs. Results In this study, we first systematically collected reported information about 26 traditional Chinese decoction pieces drugs, including their chemical ingredients and adverse events (AEs). By mostly reusing terms from existing ontologies (e.g., TCMDPO for traditional Chinese medicines, NCBITaxon for taxonomy, ChEBI for chemical elements, and OAE for adverse events) and making semantic axioms linking different entities, we developed the Ontology of Chinese Medicine for Rheumatism (OCMR) that includes over 3000 class terms. Our OCMR analysis found that these 26 traditional Chinese decoction pieces are made from anatomic entities (e.g., root and stem) from 3 Bilateria animals and 23 Mesangiospermae plants. Anti-inflammatory and antineoplastic roles are important for anti-rheumatism drugs. Using the total of 555 unique ChEBI chemical entities identified from these drugs, our ChEBI-based classification analysis identified 18 anti-inflammatory, 33 antineoplastic chemicals, and 9 chemicals (including 3 diterpenoids and 3 triterpenoids) having both anti-inflammatory and antineoplastic roles. Furthermore, our study detected 22 diterpenoids and 23 triterpenoids, including 16 pentacyclic triterpenoids that are likely bioactive against rheumatism. Six drugs were found to be associated with 184 unique AEs, including three AEs (i.e., dizziness, nausea and vomiting, and anorexia) each associated with 5 drugs. Several chemical entities are classified as neurotoxins (e.g., diethyl phthalate) and allergens (e.g., eugenol), which may explain the formation of some TCD AEs. The OCMR could be efficiently queried for useful information using SPARQL scripts. Conclusions The OCMR ontology was developed to systematically represent 26 traditional anti-rheumatism Chinese drugs and their related information. The OCMR analysis identified possible anti-rheumatism and AE mechanisms of these drugs. Our novel ontology-based approach can also be applied to systematic representation and analysis of other traditional Chinese drugs. Electronic supplementary material The online version of this article (10.1186/s12918-017-0510-5) contains supplementary material, which is available to authorized users.
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Ontology-based systematical representation and drug class effect analysis of package insert-reported adverse events associated with cardiovascular drugs used in China. Sci Rep 2017; 7:13819. [PMID: 29061976 PMCID: PMC5653862 DOI: 10.1038/s41598-017-12580-4] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2017] [Accepted: 09/07/2017] [Indexed: 01/31/2023] Open
Abstract
With increased usage of cardiovascular drugs (CVDs) for treating cardiovascular diseases, it is important to analyze CVD-associated adverse events (AEs). In this study, we systematically collected package insert-reported AEs associated with CVDs used in China, and developed and analyzed an Ontology of Cardiovascular Drug AEs (OCVDAE). Extending the Ontology of AEs (OAE) and NDF-RT, OCVDAE includes 194 CVDs, CVD ingredients, mechanisms of actions (MoAs), and CVD-associated 736 AEs. An AE-specific drug class effect is defined to exist when all the drugs (drug chemical ingredients or drug products) in a drug class are associated with an AE, which is formulated as a new proportional class level ratio (“PCR”) = 1. Our PCR-based heatmap analysis identified many class level drug effects on different AE classes such as behavioral and neurological AE and digestive system AE. Additional drug-AE correlation tests (i.e., class-level PRR, Chi-squared, and minimal case reports) were also modified and applied to further detect statistically significant drug class effects. Two drug ingredient classes and three CVD MoA classes were found to have statistically significant class effects on 13 AEs. For example, the CVD Active Transporter Interactions class (including reserpine, indapamide, digoxin, and deslanoside) has statistically significant class effect on anorexia and diarrhea AEs.
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