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Göğebakan K, Ulu R, Abiyev R, Şah M. A drug prescription recommendation system based on novel DIAKID ontology and extensive semantic rules. Health Inf Sci Syst 2024; 12:27. [PMID: 38524804 PMCID: PMC10960787 DOI: 10.1007/s13755-024-00286-7] [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: 04/10/2023] [Accepted: 02/28/2024] [Indexed: 03/26/2024] Open
Abstract
According to the World Health Organization (WHO) data from 2000 to 2019, the number of people living with Diabetes Mellitus and Chronic Kidney Disease (CKD) is increasing rapidly. It is observed that Diabetes Mellitus increased by 70% and ranked in the top 10 among all causes of death, while the rate of those who died from CKD increased by 63% and rose from the 13th place to the 10th place. In this work, we combined the drug dose prediction model, drug-drug interaction warnings and drugs that potassium raising (K-raising) warnings to create a novel and effective ontology-based assistive prescription recommendation system for patients having both Type-2 Diabetes Mellitus (T2DM) and CKD. Although there are several computational solutions that use ontology-based systems for treatment plans for these type of diseases, none of them combine information analysis and treatment plans prediction for T2DM and CKD. The proposed method is novel: (1) We develop a new drug-drug interaction model and drug dose ontology called DIAKID (for drugs of T2DM and CKD). (2) Using comprehensive Semantic Web Rule Language (SWRL) rules, we automatically extract the correct drug dose, K-raising drugs, and drug-drug interaction warnings based on the Glomerular Filtration Rate (GFR) value of T2DM and CKD patients. The proposed work achieves very competitive results, and this is the first time such a study conducted on both diseases. The proposed system will guide clinicians in preparing prescriptions by giving necessary warnings about drug-drug interactions and doses.
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Affiliation(s)
- Kadime Göğebakan
- Directorate of Information Technologies, Istanbul Technical University, North Cyprus via Mersin 10, Famagusta, Turkey
| | - Ramazan Ulu
- Department of Nephrology, School of Medicine, Adiyaman University, Adiyaman, Turkey
| | - Rahib Abiyev
- Computer Engineering Department, Near East University, North Cyprus via Mersin 10, Nicosia, Turkey
| | - Melike Şah
- Computer Engineering Department, Cyprus International University, North Cyprus via Mersin 10, Nicosia, Turkey
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Natsiavas P, Nikolaidis G, Pliatsika J, Chytas A, Giannios G, Karanikas H, Grammatikopoulou M, Zachariadou M, Dimitriadis V, Nikolopoulos S, Kompatsiaris I. The PrescIT platform: An interoperable Clinical Decision Support System for ePrescription to Prevent Adverse Drug Reactions and Drug-Drug Interactions. Drug Saf 2024; 47:1051-1059. [PMID: 39030460 DOI: 10.1007/s40264-024-01455-z] [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] [Accepted: 06/03/2024] [Indexed: 07/21/2024]
Abstract
INTRODUCTION Preventable medication errors have been proven to cause significant public health burden, and ePrescription is a key part of the process where medication errors and adverse effects could be prevented. Information systems and "intelligent" computational approaches could provide a valuable tool to prevent such errors with profound impact in clinical practice. OBJECTIVES The PrescIT platform is a Clinical Decision Support System (CDSS) that aims to facilitate the prevention of adverse drug reactions (ADRs) and drug-drug interactions (DDIs) in the phase of ePrescription in Greece. The proposed platform could be relatively easily localized for use in other contexts too. METHODS The PrescIT platform is based on the use of Knowledge Engineering (ΚΕ) approaches, i.e., the use of Ontologies and Knowledge Graphs (KGs) developed upon openly available data sources. Open standards (i.e., RDF, OWL, SPARQL) are used for the development of the platform enabling the integration with already existing IT systems or for standalone use. The main KG is based on the use of DrugBank, MedDRA, SemMedDB and OpenPVSignal. In addition, the Business Process Management Notation (BPMN) has been used to model long-term therapeutic protocols used during the ePrescription process. Finally, the produced software has been pilot tested in three hospitals by 18 clinical professionals via in-person think-aloud sessions. RESULTS The PrescIT platform has been successfully integrated in a transparent fashion in a proprietary Hospital Information System (HIS), and it has also been used as a standalone application. Furthermore, it has been successfully integrated with the Greek National ePrescription system. During the pilot phase, one psychiatric therapeutic protocol was used as a testbed to collect end-users' feedback. Summarizing the feedback from the end-users, they have generally acknowledged the usefulness of such a system while also identifying some challenges in terms of usability and the overall user experience. CONCLUSIONS The PrescIT platform has been successfully deployed and piloted in real-world environments to evaluate its ability to support safer medication prescriptions.
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Affiliation(s)
- Pantelis Natsiavas
- Institute of Applied Biosciences, Centre for Research and Technology Hellas, 6th Km. Charilaou, Thermi Road, Thermi, PO Box 60361, 57001, Thessaloniki, Greece.
| | | | | | - Achilles Chytas
- Institute of Applied Biosciences, Centre for Research and Technology Hellas, 6th Km. Charilaou, Thermi Road, Thermi, PO Box 60361, 57001, Thessaloniki, Greece
| | - George Giannios
- Information Technologies Institute, Centre for Research and Technology Hellas, 6th Km. Charilaou, Thermi Road, Thermi, PO Box 60361, 57001, Thessaloniki, Greece
| | - Haralampos Karanikas
- Department of Computer Science and Biomedical Informatics, University of Thessaly, Papasiopoulou 2-4, Postal code 35131, Lamia, Greece
| | - Margarita Grammatikopoulou
- Information Technologies Institute, Centre for Research and Technology Hellas, 6th Km. Charilaou, Thermi Road, Thermi, PO Box 60361, 57001, Thessaloniki, Greece
| | | | - Vlasios Dimitriadis
- Institute of Applied Biosciences, Centre for Research and Technology Hellas, 6th Km. Charilaou, Thermi Road, Thermi, PO Box 60361, 57001, Thessaloniki, Greece
| | - Spiros Nikolopoulos
- Information Technologies Institute, Centre for Research and Technology Hellas, 6th Km. Charilaou, Thermi Road, Thermi, PO Box 60361, 57001, Thessaloniki, Greece
| | - Ioannis Kompatsiaris
- Information Technologies Institute, Centre for Research and Technology Hellas, 6th Km. Charilaou, Thermi Road, Thermi, PO Box 60361, 57001, Thessaloniki, Greece
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Gurbuz O, Alanis-Lobato G, Picart-Armada S, Sun M, Haslinger C, Lawless N, Fernandez-Albert F. Knowledge Graphs for Indication Expansion: An Explainable Target-Disease Prediction Method. Front Genet 2022; 13:814093. [PMID: 35360842 PMCID: PMC8963915 DOI: 10.3389/fgene.2022.814093] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Accepted: 01/28/2022] [Indexed: 11/19/2022] Open
Abstract
Indication expansion aims to find new indications for existing targets in order to accelerate the process of launching a new drug for a disease on the market. The rapid increase in data types and data sources for computational drug discovery has fostered the use of semantic knowledge graphs (KGs) for indication expansion through target centric approaches, or in other words, target repositioning. Previously, we developed a novel method to construct a KG for indication expansion studies, with the aim of finding and justifying alternative indications for a target gene of interest. In contrast to other KGs, ours combines human-curated full-text literature and gene expression data from biomedical databases to encode relationships between genes, diseases, and tissues. Here, we assessed the suitability of our KG for explainable target-disease link prediction using a glass-box approach. To evaluate the predictive power of our KG, we applied shortest path with tissue information- and embedding-based prediction methods to a graph constructed with information published before or during 2010. We also obtained random baselines by applying the shortest path predictive methods to KGs with randomly shuffled node labels. Then, we evaluated the accuracy of the top predictions using gene-disease links reported after 2010. In addition, we investigated the contribution of the KG’s tissue expression entity to the prediction performance. Our experiments showed that shortest path-based methods significantly outperform the random baselines and embedding-based methods outperform the shortest path predictions. Importantly, removing the tissue expression entity from the KG severely impacts the quality of the predictions, especially those produced by the embedding approaches. Finally, since the interpretability of the predictions is crucial in indication expansion, we highlight the advantages of our glass-box model through the examination of example candidate target-disease predictions.
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Affiliation(s)
- Ozge Gurbuz
- Discovery Research Coordination Germany, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riss, Germany
- *Correspondence: Ozge Gurbuz, ; Francesc Fernandez-Albert,
| | - Gregorio Alanis-Lobato
- Global Computational Biology and Data Sciences, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riss, Germany
| | - Sergio Picart-Armada
- Global Computational Biology and Data Sciences, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riss, Germany
| | - Miao Sun
- Global Computational Biology and Data Sciences, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riss, Germany
| | - Christian Haslinger
- Global Computational Biology and Data Sciences, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riss, Germany
| | - Nathan Lawless
- Global Computational Biology and Data Sciences, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riss, Germany
| | - Francesc Fernandez-Albert
- Global Computational Biology and Data Sciences, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riss, Germany
- *Correspondence: Ozge Gurbuz, ; Francesc Fernandez-Albert,
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Egami S, Kawamura T, Kozaki K, Ohsuga A. Detecting Vicious Cycles in Urban Problem Knowledge Graph using
Inference Rules. DATA INTELLIGENCE 2022. [DOI: 10.1162/dint_a_00113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
Abstract
Urban areas have many problems, including homelessness, graffiti, and littering. These problems are influenced by various factors and are linked to each other; thus, an understanding of the problem structure is required in order to detect and solve the root problems that generate vicious cycles. Moreover, before implementing action plans to solve these problems, local governments need to estimate cost-effectiveness when the plans are carried out. Therefore, this paper proposed constructing an urban problem knowledge graph that would include urban problems' causality and the related cost information in budget sheets. In addition, this paper proposed a method for detecting vicious cycles of urban problems using SPARQL queries with inference rules from the knowledge graph. Finally, several root problems that led to vicious cycles were detected. Urban-problem experts evaluated the extracted causal relations.
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Affiliation(s)
- Shusaku Egami
- National Institute of Advanced Industrial Science and Technology (AIST), Tokyo 135-0064, Japan
- Graduate School of Informatics and Engineering, The University of Electro-Communications, Tokyo 182-8585, Japan
| | - Takahiro Kawamura
- National Institute of Advanced Industrial Science and Technology (AIST), Tokyo 135-0064, Japan
- National Agriculture and Food Research Organization, Ibaraki 305-8517, Japan
| | - Kouji Kozaki
- National Institute of Advanced Industrial Science and Technology (AIST), Tokyo 135-0064, Japan
- Faculty of Information and Communication Engineering, Osaka Electro-Communication University, Osaka 572-8530, Japan
| | - Akihiko Ohsuga
- Graduate School of Informatics and Engineering, The University of Electro-Communications, Tokyo 182-8585, Japan
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Hastings J, Glauer M, Memariani A, Neuhaus F, Mossakowski T. Learning chemistry: exploring the suitability of machine learning for the task of structure-based chemical ontology classification. J Cheminform 2021; 13:23. [PMID: 33726837 PMCID: PMC7962259 DOI: 10.1186/s13321-021-00500-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Accepted: 02/26/2021] [Indexed: 12/22/2022] Open
Abstract
Chemical data is increasingly openly available in databases such as PubChem, which contains approximately 110 million compound entries as of February 2021. With the availability of data at such scale, the burden has shifted to organisation, analysis and interpretation. Chemical ontologies provide structured classifications of chemical entities that can be used for navigation and filtering of the large chemical space. ChEBI is a prominent example of a chemical ontology, widely used in life science contexts. However, ChEBI is manually maintained and as such cannot easily scale to the full scope of public chemical data. There is a need for tools that are able to automatically classify chemical data into chemical ontologies, which can be framed as a hierarchical multi-class classification problem. In this paper we evaluate machine learning approaches for this task, comparing different learning frameworks including logistic regression, decision trees and long short-term memory artificial neural networks, and different encoding approaches for the chemical structures, including cheminformatics fingerprints and character-based encoding from chemical line notation representations. We find that classical learning approaches such as logistic regression perform well with sets of relatively specific, disjoint chemical classes, while the neural network is able to handle larger sets of overlapping classes but needs more examples per class to learn from, and is not able to make a class prediction for every molecule. Future work will explore hybrid and ensemble approaches, as well as alternative network architectures including neuro-symbolic approaches.
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Affiliation(s)
- Janna Hastings
- Department of Computer Science, Otto-von-Guericke University of Magdeburg, Magdeburg, Germany
| | - Martin Glauer
- Department of Computer Science, Otto-von-Guericke University of Magdeburg, Magdeburg, Germany
| | - Adel Memariani
- Department of Computer Science, Otto-von-Guericke University of Magdeburg, Magdeburg, Germany
| | - Fabian Neuhaus
- Department of Computer Science, Otto-von-Guericke University of Magdeburg, Magdeburg, Germany
| | - Till Mossakowski
- Department of Computer Science, Otto-von-Guericke University of Magdeburg, Magdeburg, Germany
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6
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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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7
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Chary MA, Manini AF, Boyer EW, Burns M. The Role and Promise of Artificial Intelligence in Medical Toxicology. J Med Toxicol 2020; 16:458-464. [PMID: 32215849 PMCID: PMC7554271 DOI: 10.1007/s13181-020-00769-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Revised: 02/03/2020] [Accepted: 03/05/2020] [Indexed: 12/23/2022] Open
Abstract
Artificial intelligence (AI) refers to machines or software that process information and interact with the world as understanding beings. Examples of AI in medicine include the automated reading of chest X-rays and the detection of heart dysrhythmias from wearables. A key promise of AI is its potential to apply logical reasoning at the scale of data too vast for the human mind to comprehend. This scaling up of logical reasoning may allow clinicians to bring the entire breadth of current medical knowledge to bear on each patient in real time. It may also unearth otherwise unreachable knowledge in the attempt to integrate knowledge and research across disciplines. In this review, we discuss two complementary aspects of artificial intelligence: deep learning and knowledge representation. Deep learning recognizes and predicts patterns. Knowledge representation structures and interprets those patterns or predictions. We frame this review around how deep learning and knowledge representation might expand the reach of Poison Control Centers and enhance syndromic surveillance from social media.
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Affiliation(s)
- Michael A Chary
- Harvard Medical Toxicology Program, Department of Emergency Medicine, Boston Children's Hospital, Boston, MA, USA.
- Department of Emergency Medicine, Brigham and Women's Hospital, Boston, MA, USA.
| | - Alex F Manini
- Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Edward W Boyer
- Department of Emergency Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Michele Burns
- Harvard Medical Toxicology Program, Department of Emergency Medicine, Boston Children's Hospital, Boston, MA, USA
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8
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Assiri A, Noor A. A computational approach to predict multi-pathway drug-drug interactions: A case study of irinotecan, a colon cancer medication. Saudi Pharm J 2020; 28:1507-1513. [PMID: 33424244 PMCID: PMC7783232 DOI: 10.1016/j.jsps.2020.09.017] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Accepted: 09/18/2020] [Indexed: 01/03/2023] Open
Abstract
Drug-drug interactions (DDIs) are a potentially distressing corollary of drug interventions, and may result in discomfort, debilitating illness, or even death. Existing research predominantly considers only a single level of interaction; however, serious health complications may result from multi-pathway DDIs, and so new methods are needed to enable predicting and preventing complex DDIs. This article introduces a novel method for the prediction of DDIs at two pharmacological levels (metabolic and transporter interactions) by means of a rule-based model implemented with Semantic Web technologies. The chemotherapy agent irinotecan is used as a case study for demonstrating the validity of this approach. Mechanistic and interaction data were mined from available sources and then used to predict interactors of irinotecan, including potential DDIs mediated by previously unidentified mechanisms. The findings also draw attention to the profound variation between DDI resources, indicating that clinical practice would see significant value from the development of an evidence-based resource to support DDI identification.
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Affiliation(s)
- Abdullah Assiri
- Department of Clinical Pharmacy, College of Pharmacy, King Khalid University, Abha 62529, Saudi Arabia
| | - Adeeb Noor
- Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 80221, Saudi Arabia
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9
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Bassiliades N. A Tool for Transforming Semantic Web Rule Language to SPARQL Infererecing Notation. INT J SEMANT WEB INF 2020. [DOI: 10.4018/ijswis.2020010105] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Semantic web rule language (SWRL) combines web ontology language (OWL) ontologies with horn logic rules of the rule markup language (RuleML) family. Being supported by ontology editors, rule engines and ontology reasoners, it has become a very popular choice for developing rule-based applications on top of ontologies. However, SWRL is probably not going to become a WWW Consortium standard, prohibiting industrial acceptance. On the other hand, SPARQL Inferencing Notation (SPIN) has become a de-facto industry standard to represent SPARQL rules and constraints on semantic web models, building on the widespread acceptance of SPARQL (SPARQL Protocol and RDF Query Language). In this article, we argue that the life of existing SWRL rule-based ontology applications can be prolonged by converting them to SPIN. To this end, we have developed the SWRL2SPIN tool in Prolog that transforms SWRL rules into SPIN rules, considering the object-orientation of SPIN, i.e. linking rules to the appropriate ontology classes and optimizing them, as derived by analysing the rule conditions.
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10
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Yan C, Duan G, Pan Y, Wu FX, Wang J. DDIGIP: predicting drug-drug interactions based on Gaussian interaction profile kernels. BMC Bioinformatics 2019; 20:538. [PMID: 31874609 PMCID: PMC6929542 DOI: 10.1186/s12859-019-3093-x] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2019] [Accepted: 09/10/2019] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND A drug-drug interaction (DDI) is defined as a drug effect modified by another drug, which is very common in treating complex diseases such as cancer. Many studies have evidenced that some DDIs could be an increase or a decrease of the drug effect. However, the adverse DDIs maybe result in severe morbidity and even morality of patients, which also cause some drugs to withdraw from the market. As the multi-drug treatment becomes more and more common, identifying the potential DDIs has become the key issue in drug development and disease treatment. However, traditional biological experimental methods, including in vitro and vivo, are very time-consuming and expensive to validate new DDIs. With the development of high-throughput sequencing technology, many pharmaceutical studies and various bioinformatics data provide unprecedented opportunities to study DDIs. RESULT In this study, we propose a method to predict new DDIs, namely DDIGIP, which is based on Gaussian Interaction Profile (GIP) kernel on the drug-drug interaction profiles and the Regularized Least Squares (RLS) classifier. In addition, we also use the k-nearest neighbors (KNN) to calculate the initial relational score in the presence of new drugs via the chemical, biological, phenotypic data of drugs. We compare the prediction performance of DDIGIP with other competing methods via the 5-fold cross validation, 10-cross validation and de novo drug validation. CONLUSION In 5-fold cross validation and 10-cross validation, DDRGIP method achieves the area under the ROC curve (AUC) of 0.9600 and 0.9636 which are better than state-of-the-art method (L1 Classifier ensemble method) of 0.9570 and 0.9599. Furthermore, for new drugs, the AUC value of DDIGIP in de novo drug validation reaches 0.9262 which also outperforms the other state-of-the-art method (Weighted average ensemble method) of 0.9073. Case studies and these results demonstrate that DDRGIP is an effective method to predict DDIs while being beneficial to drug development and disease treatment.
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Affiliation(s)
- Cheng Yan
- School of Computer Science and Engineering, Central South University, 932 South Lushan Rd, ChangSha, 410083 China
- School of Computer and Information,Qiannan Normal University for Nationalities, Longshan Road, DuYun, 558000 China
| | - Guihua Duan
- School of Computer Science and Engineering, Central South University, 932 South Lushan Rd, ChangSha, 410083 China
| | - Yi Pan
- Department of Computer Science, Georgia State University, Atlanta, GA30302 USA
| | - Fang-Xiang Wu
- Biomedical Engineering and Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SKS7N5A9 Canada
| | - Jianxin Wang
- School of Computer Science and Engineering, Central South University, 932 South Lushan Rd, ChangSha, 410083 China
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11
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Bona JP, Prior FW, Zozus MN, Brochhausen M. Enhancing Clinical Data and Clinical Research Data with Biomedical Ontologies - Insights from the Knowledge Representation Perspective. Yearb Med Inform 2019; 28:140-151. [PMID: 31419826 PMCID: PMC6697506 DOI: 10.1055/s-0039-1677912] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
Abstract
OBJECTIVES There exists a communication gap between the biomedical informatics community on one side and the computer science/artificial intelligence community on the other side regarding the meaning of the terms "semantic integration" and "knowledge representation". This gap leads to approaches that attempt to provide one-to-one mappings between data elements and biomedical ontologies. Our aim is to clarify the representational differences between traditional data management and semantic-web-based data management by providing use cases of clinical data and clinical research data re-representation. We discuss how and why one-to-one mappings limit the advantages of using Semantic Web Technologies (SWTs). METHODS We employ commonly used SWTs, such as Resource Description Framework (RDF) and Ontology Web Language (OWL). We reuse pre-existing ontologies and ensure shared ontological commitment by selecting ontologies from a framework that fosters community-driven collaborative ontology development for biomedicine following the same set of principles. RESULTS We demonstrate the results of providing SWT-compliant re-representation of data elements from two independent projects managing clinical data and clinical research data. Our results show how one-to-one mappings would hinder the exploitation of the advantages provided by using SWT. CONCLUSIONS We conclude that SWT-compliant re-representation is an indispensable step, if using the full potential of SWT is the goal. Rather than providing one-to-one mappings, developers should provide documentation that links data elements to graph structures to specify the re-representation.
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Affiliation(s)
| | - Fred W. Prior
- University of Arkansas for Medical Sciences, Arkansas, USA
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12
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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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13
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Kanza S, Frey JG. A new wave of innovation in Semantic web tools for drug discovery. Expert Opin Drug Discov 2019; 14:433-444. [DOI: 10.1080/17460441.2019.1586880] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Samantha Kanza
- Department of Chemistry, Highfield Campus, University of Southampton, Southampton, UK
| | - Jeremy Graham Frey
- Department of Chemistry, Highfield Campus, University of Southampton, Southampton, UK
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14
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Vilar S, Friedman C, Hripcsak G. Detection of drug-drug interactions through data mining studies using clinical sources, scientific literature and social media. Brief Bioinform 2018; 19:863-877. [PMID: 28334070 PMCID: PMC6454455 DOI: 10.1093/bib/bbx010] [Citation(s) in RCA: 86] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2016] [Revised: 12/28/2016] [Indexed: 11/13/2022] Open
Abstract
Drug-drug interactions (DDIs) constitute an important concern in drug development and postmarketing pharmacovigilance. They are considered the cause of many adverse drug effects exposing patients to higher risks and increasing public health system costs. Methods to follow-up and discover possible DDIs causing harm to the population are a primary aim of drug safety researchers. Here, we review different methodologies and recent advances using data mining to detect DDIs with impact on patients. We focus on data mining of different pharmacovigilance sources, such as the US Food and Drug Administration Adverse Event Reporting System and electronic health records from medical institutions, as well as on the diverse data mining studies that use narrative text available in the scientific biomedical literature and social media. We pay attention to the strengths but also further explain challenges related to these methods. Data mining has important applications in the analysis of DDIs showing the impact of the interactions as a cause of adverse effects, extracting interactions to create knowledge data sets and gold standards and in the discovery of novel and dangerous DDIs.
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Affiliation(s)
- Santiago Vilar
- Department of Biomedical Informatics, Columbia University, New York, USA
- Department of Organic Chemistry, University of Santiago de Compostela, Spain
| | - Carol Friedman
- Department of Biomedical Informatics, Columbia University, New York, USA
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University, New York, USA
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15
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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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16
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Noor A, Assiri A, Ayvaz S, Clark C, Dumontier M. Drug-drug interaction discovery and demystification using Semantic Web technologies. J Am Med Inform Assoc 2017; 24:556-564. [PMID: 28031284 DOI: 10.1093/jamia/ocw128] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2016] [Accepted: 07/25/2016] [Indexed: 12/27/2022] Open
Abstract
Objective To develop a novel pharmacovigilance inferential framework to infer mechanistic explanations for asserted drug-drug interactions (DDIs) and deduce potential DDIs. Materials and Methods A mechanism-based DDI knowledge base was constructed by integrating knowledge from several existing sources at the pharmacokinetic, pharmacodynamic, pharmacogenetic, and multipathway interaction levels. A query-based framework was then created to utilize this integrated knowledge base in conjunction with 9 inference rules to infer mechanistic explanations for asserted DDIs and deduce potential DDIs. Results The drug-drug interactions discovery and demystification (D3) system achieved an overall 85% recall rate in terms of inferring mechanistic explanations for the DDIs integrated into its knowledge base, while demonstrating a 61% precision rate in terms of the inference or lack of inference of mechanistic explanations for a balanced, randomly selected collection of interacting and noninteracting drug pairs. Discussion The successful demonstration of the D3 system's ability to confirm interactions involving well-studied drugs enhances confidence in its ability to deduce interactions involving less-studied drugs. In its demonstration, the D3 system infers putative explanations for most of its integrated DDIs. Further enhancements to this work in the future might include ranking interaction mechanisms based on likelihood of applicability, determining the likelihood of deduced DDIs, and making the framework publicly available. Conclusion The D3 system provides an early-warning framework for augmenting knowledge of known DDIs and deducing unknown DDIs. It shows promise in suggesting interaction pathways of research and evaluation interest and aiding clinicians in evaluating and adjusting courses of drug therapy.
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Affiliation(s)
- Adeeb Noor
- Faculty of Computing and Information Technology, King Abdul Aziz University, Jeddah, KSA
| | - Abdullah Assiri
- School of Pharmacy, Purdue University, West Lafayette, Indiana, USA.,School of Pharmacy, King Khalid University, Abha, KSA
| | - Serkan Ayvaz
- Department of Computer Engineering, Bahcesehir University, Besiktas, Istanbul 34353, Turkey
| | - Connor Clark
- Unaffiliated Researcher, Mountain View, California, USA
| | - Michel Dumontier
- Stanford Center for Biomedical Informatics Research, Stanford, California
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17
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A Knowledge-Driven Geospatially Enabled Framework for Geological Big Data. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 2017. [DOI: 10.3390/ijgi6060166] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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18
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Takeda T, Hao M, Cheng T, Bryant SH, Wang Y. Predicting drug-drug interactions through drug structural similarities and interaction networks incorporating pharmacokinetics and pharmacodynamics knowledge. J Cheminform 2017; 9:16. [PMID: 28316654 PMCID: PMC5340788 DOI: 10.1186/s13321-017-0200-8] [Citation(s) in RCA: 52] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2016] [Accepted: 02/15/2017] [Indexed: 12/19/2022] Open
Abstract
Drug–drug interactions (DDIs) may lead to adverse effects and potentially result in drug withdrawal from the market. Predicting DDIs during drug development would help reduce development costs and time by rigorous evaluation of drug candidates. The primary mechanisms of DDIs are based on pharmacokinetics (PK) and pharmacodynamics (PD). This study examines the effects of 2D structural similarities of drugs on DDI prediction through interaction networks including both PD and PK knowledge. Our assumption was that a query drug (Dq) and a drug to be examined (De) likely have DDI if the drugs in the interaction network of De are structurally similar to Dq. A network of De describes the associations between the drugs and the proteins relating to PK and PD for De. These include target proteins, proteins interacting with target proteins, enzymes, and transporters for De. We constructed logistic regression models for DDI prediction using only 2D structural similarities between each Dq and the drugs in the network of De. The results indicated that our models could effectively predict DDIs. It was found that integrating structural similarity scores of the drugs relating to both PK and PD of De was crucial for model performance. In particular, the combination of the target- and enzyme-related scores provided the largest increase of the predictive power.. ![]()
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Affiliation(s)
- Takako Takeda
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894 USA
| | - Ming Hao
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894 USA
| | - Tiejun Cheng
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894 USA
| | - Stephen H Bryant
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894 USA
| | - Yanli Wang
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894 USA
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19
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Herrero-Zazo M, Segura-Bedmar I, Martínez P. Conceptual models of drug-drug interactions: A summary of recent efforts. Knowl Based Syst 2016. [DOI: 10.1016/j.knosys.2016.10.006] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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20
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Segura-Bedmar I, Martínez P. Pharmacovigilance through the development of text mining and natural language processing techniques. J Biomed Inform 2015; 58:288-291. [PMID: 26547007 DOI: 10.1016/j.jbi.2015.11.001] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
| | - Paloma Martínez
- Computer Science Department, Universidad Carlos III de Madrid, Spain.
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