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Silva MC, Eugénio P, Faria D, Pesquita C. Ontologies and Knowledge Graphs in Oncology Research. Cancers (Basel) 2022; 14:cancers14081906. [PMID: 35454813 PMCID: PMC9029532 DOI: 10.3390/cancers14081906] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 03/25/2022] [Accepted: 04/07/2022] [Indexed: 11/16/2022] Open
Abstract
The complexity of cancer research stems from leaning on several biomedical disciplines for relevant sources of data, many of which are complex in their own right. A holistic view of cancer—which is critical for precision medicine approaches—hinges on integrating a variety of heterogeneous data sources under a cohesive knowledge model, a role which biomedical ontologies can fill. This study reviews the application of ontologies and knowledge graphs in cancer research. In total, our review encompasses 141 published works, which we categorized under 14 hierarchical categories according to their usage of ontologies and knowledge graphs. We also review the most commonly used ontologies and newly developed ones. Our review highlights the growing traction of ontologies in biomedical research in general, and cancer research in particular. Ontologies enable data accessibility, interoperability and integration, support data analysis, facilitate data interpretation and data mining, and more recently, with the emergence of the knowledge graph paradigm, support the application of Artificial Intelligence methods to unlock new knowledge from a holistic view of the available large volumes of heterogeneous data.
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Nicholson NC, Giusti F, Bettio M, Carvalho RN, Dimitrova N, Dyba T, Flego M, Neamtiu L, Randi G, Martos C. A multipurpose TNM stage ontology for cancer registries. J Biomed Semantics 2022; 13:7. [PMID: 35193690 PMCID: PMC8862240 DOI: 10.1186/s13326-022-00260-w] [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: 02/12/2021] [Accepted: 01/19/2022] [Indexed: 11/25/2022] Open
Abstract
Background Population-based cancer registries are a critical reference source for the surveillance and control of cancer. Cancer registries work extensively with the internationally recognised TNM classification system used to stage solid tumours, but the system is complex and compounded by the different TNM editions in concurrent use. TNM ontologies exist but the design requirements are different for the needs of the clinical and cancer-registry domains. Two TNM ontologies developed specifically for cancer registries were designed for different purposes and have limitations for serving wider application. A unified ontology is proposed to serve the various cancer registry TNM-related tasks and reduce the multiplication effects of different ontologies serving specific tasks. The ontology is comprehensive of the rules for TNM edition 7 as required by cancer registries and designed on a modular basis to allow extension to other TNM editions. Results A unified ontology was developed building on the experience and design of the existing ontologies. It follows a modular approach allowing plug in of components dependent upon any particular TNM edition. A Java front-end was developed to interface with the ontology via the Web Ontology Language application programme interface and enables batch validation or classification of cancer registry records. The programme also allows the means of automated error correction in some instances. Initial tests verified the design concept by correctly inferring TNM stage and successfully handling the TNM-related validation checks on a number of cancer case records, with a performance similar to that of an existing ontology dedicated to the task. Conclusions The unified ontology provides a multi-purpose tool for TNM-related tasks in a cancer registry and is scalable for different editions of TNM. It offers a convenient way of quickly checking validity of cancer case stage information and for batch processing of multi-record data via a dedicated front-end programme. The ontology is adaptable to many uses, either as a standalone TNM module or as a component in applications of wider focus. It provides a first step towards a single, unified TNM ontology for cancer registries.
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Affiliation(s)
| | | | - Manola Bettio
- European Commission Joint Research Centre, Ispra, Italy
| | | | | | - Tadeusz Dyba
- European Commission Joint Research Centre, Ispra, Italy
| | - Manuela Flego
- European Commission Joint Research Centre, Ispra, Italy
| | | | - Giorgia Randi
- European Commission Joint Research Centre, Ispra, Italy
| | - Carmen Martos
- European Commission Joint Research Centre, Ispra, Italy
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Ruiz-Saavedra S, García-González H, Arboleya S, Salazar N, Emilio Labra-Gayo J, Díaz I, Gueimonde M, González S, de los Reyes-Gavilán CG. Intestinal microbiota alterations by dietary exposure to chemicals from food cooking and processing. Application of data science for risk prediction. Comput Struct Biotechnol J 2021; 19:1081-1091. [PMID: 33680352 PMCID: PMC7892627 DOI: 10.1016/j.csbj.2021.01.037] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Revised: 01/22/2021] [Accepted: 01/22/2021] [Indexed: 01/07/2023] Open
Abstract
Diet is one of the main sources of exposure to toxic chemicals with carcinogenic potential, some of which are generated during food processing, depending on the type of food (primarily meat, fish, bread and potatoes), cooking methods and temperature. Although demonstrated in animal models at high doses, an unequivocal link between dietary exposure to these compounds with disease has not been proven in humans. A major difficulty in assessing the actual intake of these toxic compounds is the lack of standardised and harmonised protocols for collecting and analysing dietary information. The intestinal microbiota (IM) has a great influence on health and is altered in some diseases such as colorectal cancer (CRC). Diet influences the composition and activity of the IM, and the net exposure to genotoxicity of potential dietary carcinogens in the gut depends on the interaction among these compounds, IM and diet. This review analyses critically the difficulties and challenges in the study of interactions among these three actors on the onset of CRC. Machine Learning (ML) of data obtained in subclinical and precancerous stages would help to establish risk thresholds for the intake of toxic compounds generated during food processing as related to diet and IM profiles, whereas Semantic Web could improve data accessibility and usability from different studies, as well as helping to elucidate novel interactions among those chemicals, IM and diet.
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Affiliation(s)
- Sergio Ruiz-Saavedra
- Department of Microbiology and Biochemistry of Dairy Products, Instituto de Productos Lácteos de Asturias (IPLA-CSIC), 33300 Villaviciosa, Asturias, Spain
- Department of Functional Biology, University of Oviedo, 33006 Oviedo, Asturias, Spain
- Diet, Microbiota and Health Group, Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), 33011 Oviedo, Spain
| | - Herminio García-González
- Department of Computer Science, University of Oviedo, C/ Federico García Lorca S/N, 33007 Oviedo, Asturias, Spain
- IT and Communications Service, University of Oviedo, C/ Fernando Bongera S/N, 33006 Oviedo, Asturias, Spain
| | - Silvia Arboleya
- Department of Microbiology and Biochemistry of Dairy Products, Instituto de Productos Lácteos de Asturias (IPLA-CSIC), 33300 Villaviciosa, Asturias, Spain
- Diet, Microbiota and Health Group, Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), 33011 Oviedo, Spain
| | - Nuria Salazar
- Department of Microbiology and Biochemistry of Dairy Products, Instituto de Productos Lácteos de Asturias (IPLA-CSIC), 33300 Villaviciosa, Asturias, Spain
- Diet, Microbiota and Health Group, Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), 33011 Oviedo, Spain
| | - José Emilio Labra-Gayo
- Department of Computer Science, University of Oviedo, C/ Federico García Lorca S/N, 33007 Oviedo, Asturias, Spain
| | - Irene Díaz
- Department of Computer Science, University of Oviedo, C/ Federico García Lorca S/N, 33007 Oviedo, Asturias, Spain
| | - Miguel Gueimonde
- Department of Microbiology and Biochemistry of Dairy Products, Instituto de Productos Lácteos de Asturias (IPLA-CSIC), 33300 Villaviciosa, Asturias, Spain
- Diet, Microbiota and Health Group, Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), 33011 Oviedo, Spain
| | - Sonia González
- Department of Functional Biology, University of Oviedo, 33006 Oviedo, Asturias, Spain
- Diet, Microbiota and Health Group, Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), 33011 Oviedo, Spain
| | - Clara G. de los Reyes-Gavilán
- Department of Microbiology and Biochemistry of Dairy Products, Instituto de Productos Lácteos de Asturias (IPLA-CSIC), 33300 Villaviciosa, Asturias, Spain
- Diet, Microbiota and Health Group, Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), 33011 Oviedo, Spain
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Nicholson NC, Giusti F, Bettio M, Negrao Carvalho R, Dimitrova N, Dyba T, Flego M, Neamtiu L, Randi G, Martos C. An ontology-based approach for developing a harmonised data-validation tool for European cancer registration. J Biomed Semantics 2021; 12:1. [PMID: 33407816 PMCID: PMC7789225 DOI: 10.1186/s13326-020-00233-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Accepted: 11/15/2020] [Indexed: 11/10/2022] Open
Abstract
Background Population-based cancer registries constitute an important information source in cancer epidemiology. Studies collating and comparing data across regional and national boundaries have proved important for deploying and evaluating effective cancer-control strategies. A critical aspect in correctly comparing cancer indicators across regional and national boundaries lies in ensuring a good and harmonised level of data quality, which is a primary motivator for a centralised collection of pseudonymised data. The recent introduction of the European Union’s general data-protection regulation (GDPR) imposes stricter conditions on the collection, processing, and sharing of personal data. It also considers pseudonymised data as personal data. The new regulation motivates the need to find solutions that allow a continuation of the smooth processes leading to harmonised European cancer-registry data. One element in this regard would be the availability of a data-validation software tool based on a formalised depiction of the harmonised data-validation rules, allowing an eventual devolution of the data-validation process to the local level. Results A semantic data model was derived from the data-validation rules for harmonising cancer-data variables at European level. The data model was encapsulated in an ontology developed using the Web-Ontology Language (OWL) with the data-model entities forming the main OWL classes. The data-validation rules were added as axioms in the ontology. The reasoning function of the resulting ontology demonstrated its ability to trap registry-coding errors and in some instances to be able to correct errors. Conclusions Describing the European cancer-registry core data set in terms of an OWL ontology affords a tool based on a formalised set of axioms for validating a cancer-registry’s data set according to harmonised, supra-national rules. The fact that the data checks are inherently linked to the data model would lead to less maintenance overheads and also allow automatic versioning synchronisation, important for distributed data-quality checking processes.
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Affiliation(s)
| | - Francesco Giusti
- European Commission, Joint Research Centre, Via E. Fermi 2749, I-21027, Ispra, VA, Italy
| | - Manola Bettio
- European Commission, Joint Research Centre, Via E. Fermi 2749, I-21027, Ispra, VA, Italy
| | - Raquel Negrao Carvalho
- European Commission, Joint Research Centre, Via E. Fermi 2749, I-21027, Ispra, VA, Italy
| | - Nadya Dimitrova
- European Commission, Joint Research Centre, Via E. Fermi 2749, I-21027, Ispra, VA, Italy
| | - Tadeusz Dyba
- European Commission, Joint Research Centre, Via E. Fermi 2749, I-21027, Ispra, VA, Italy
| | - Manuela Flego
- European Commission, Joint Research Centre, Via E. Fermi 2749, I-21027, Ispra, VA, Italy
| | - Luciana Neamtiu
- European Commission, Joint Research Centre, Via E. Fermi 2749, I-21027, Ispra, VA, Italy
| | - Giorgia Randi
- European Commission, Joint Research Centre, Via E. Fermi 2749, I-21027, Ispra, VA, Italy
| | - Carmen Martos
- European Commission, Joint Research Centre, Via E. Fermi 2749, I-21027, Ispra, VA, Italy
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Hasan SMS, Rivera D, Wu XC, Durbin EB, Christian JB, Tourassi G. Knowledge Graph-Enabled Cancer Data Analytics. IEEE J Biomed Health Inform 2020; 24:1952-1967. [PMID: 32386166 PMCID: PMC8324069 DOI: 10.1109/jbhi.2020.2990797] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Cancer registries collect unstructured and structured cancer data for surveillance purposes which provide important insights regarding cancer characteristics, treatments, and outcomes. Cancer registry data typically (1) categorize each reportable cancer case or tumor at the time of diagnosis, (2) contain demographic information about the patient such as age, gender, and location at time of diagnosis, (3) include planned and completed primary treatment information, and (4) may contain survival outcomes. As structured data is being extracted from various unstructured sources, such as pathology reports, radiology reports, medical records, and stored for reporting and other needs, the associated information representing a reportable cancer is constantly expanding and evolving. While some popular analytic approaches including SEER*Stat and SAS exist, we provide a knowledge graph approach to organizing cancer registry data. Our approach offers unique advantages for timely data analysis and presentation and visualization of valuable information. This knowledge graph approach semantically enriches the data, and easily enables linking with third-party data which can help explain variation in cancer incidence patterns, disparities, and outcomes. We developed a prototype knowledge graph based on the Louisiana Tumor Registry dataset. We present the advantages of the knowledge graph approach by examining: i) scenario-specific queries, ii) links with openly available external datasets, iii) schema evolution for iterative analysis, and iv) data visualization. Our results demonstrate that this graph based solution can perform complex queries, improve query run-time performance by up to 76%, and more easily conduct iterative analyses to enhance researchers' understanding of cancer registry data.
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Esteban-Gil A, Pérez-Sanz F, García-Solano J, Alburquerque-González B, Parreño-González MA, Legaz-García MDC, Fernández-Breis JT, Rodriguez-Braun E, Pimentel P, Tuomisto A, Mäkinen M, Slaby O, Conesa-Zamora P. ColPortal, an integrative multiomic platform for analysing epigenetic interactions in colorectal cancer. Sci Data 2019; 6:255. [PMID: 31672979 PMCID: PMC6823353 DOI: 10.1038/s41597-019-0198-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Accepted: 09/06/2019] [Indexed: 12/21/2022] Open
Abstract
Colorectal cancer (CRC) is the third leading cause of cancer mortality worldwide. Different pathological pathways and molecular drivers have been described and some of the associated markers are used to select effective anti-neoplastic therapy. More recent evidence points to a causal role of microbiota and altered microRNA expression in CRC carcinogenesis, but their relationship with pathological drivers or molecular phenotypes is not clearly established. Joint analysis of clinical and omics data can help clarify such relations. We present ColPortal, a platform that integrates transcriptomic, microtranscriptomic, methylomic and microbiota data of patients with colorectal cancer. ColPortal also includes detailed information of histological features and digital histological slides from the study cases, since histology is a morphological manifestation of a complex molecular change. The current cohort consists of Caucasian patients from Europe. For each patient, demographic information, location, histology, tumor staging, tissue prognostic factors, molecular biomarker status and clinical outcomes are integrated with omics data. ColPortal allows one to perform multiomics analyses for groups of patients selected by their clinical data. Measurement(s) | miRNA • methylation • clinical history • histology • transcription profiling assay • microbiome | Technology Type(s) | DNA sequencing • clinical monitoring • RNA sequencing • amplicon sequencing • ex vivo photography with digital image analysis • methylation profiling by array | Factor Type(s) | tumor status | Sample Characteristic - Organism | Homo sapiens |
Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.9785795
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Affiliation(s)
- Angel Esteban-Gil
- Biomedical Informatics & Bioinformatics Platform, Institute for Biomedical Research of Murcia (IMIB)/Foundation for Healthcare Training & Research of the Region of Murcia (FFIS), Calle Luis Fontes Pagán 9, 30003, Murcia, Spain.
| | - Fernando Pérez-Sanz
- Biomedical Informatics & Bioinformatics Platform, Institute for Biomedical Research of Murcia (IMIB)/Foundation for Healthcare Training & Research of the Region of Murcia (FFIS), Calle Luis Fontes Pagán 9, 30003, Murcia, Spain
| | - José García-Solano
- Department of Pathology, Santa Lucía General University Hospital (HGUSL), Calle Mezquita sn, 30202, Cartagena, Spain.,Department of Histology and Pathology, Faculty of Life Sciences, Catholic University of Murcia (UCAM), Murcia, Spain.,Research Group on Molecular Pathology and Pharmacogenetics, Institute for Biomedical Research of Murcia (IMIB), Calle Mezquita sn, 30202, Cartagena, Spain
| | - Begoña Alburquerque-González
- Department of Histology and Pathology, Faculty of Life Sciences, Catholic University of Murcia (UCAM), Murcia, Spain
| | - María Antonia Parreño-González
- Biomedical Informatics & Bioinformatics Platform, Institute for Biomedical Research of Murcia (IMIB)/Foundation for Healthcare Training & Research of the Region of Murcia (FFIS), Calle Luis Fontes Pagán 9, 30003, Murcia, Spain
| | - María Del Carmen Legaz-García
- Biomedical Informatics & Bioinformatics Platform, Institute for Biomedical Research of Murcia (IMIB)/Foundation for Healthcare Training & Research of the Region of Murcia (FFIS), Calle Luis Fontes Pagán 9, 30003, Murcia, Spain
| | | | | | - Paola Pimentel
- Department of Oncology, HGUSL, Calle Mezquita sn, 30202, Cartagena, Spain
| | - Anne Tuomisto
- Department of Pathology, University of Oulu, Aapistie, 9, 90014, Oulu, Finland
| | - Markus Mäkinen
- Department of Pathology, University of Oulu, Aapistie, 9, 90014, Oulu, Finland
| | - Ondrej Slaby
- Central European Institute of Technology, Masaryk University/Department of Comprehensive Cancer Care, Masaryk Memorial Cancer Institute, Faculty of Medicine, Masaryk University, Kamenice 753/5, 625 00, Brno, Czech Republic
| | - Pablo Conesa-Zamora
- Department of Histology and Pathology, Faculty of Life Sciences, Catholic University of Murcia (UCAM), Murcia, Spain. .,Research Group on Molecular Pathology and Pharmacogenetics, Institute for Biomedical Research of Murcia (IMIB), Calle Mezquita sn, 30202, Cartagena, Spain. .,Department of Laboratory Medicine, HGUSL, Cartagena, Spain.
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Pop B, Fetica B, Blaga ML, Trifa AP, Achimas-Cadariu P, Vlad CI, Achimas-Cadariu A. The role of medical registries, potential applications and limitations. Med Pharm Rep 2019; 92:7-14. [PMID: 30957080 PMCID: PMC6448488 DOI: 10.15386/cjmed-1015] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2018] [Revised: 07/19/2018] [Accepted: 07/23/2018] [Indexed: 12/14/2022] Open
Abstract
Medical registries provide highly reliable data, challenged hierarchically only by randomized controlled trials. Although registries have been used in several fields of medicine for more than a century and a half, their key role is frequently overlooked and poorly recognized. Medical registries have evolved from calculating basic epidemiological data (incidence, prevalence, mortality) to diverse applications in disease prevention, early diagnosis and screening programs, treatment response, health care planning, decision making and disease control programs. Implementing, maintaining and running a medical registry requires substantial effort. Developing the registry represents a complex task and is one of the major barriers in widespread use of registries. Medical registries have potential to evolve to a next generation by taking benefit from recent semantic web technology developments. This paper is aimed at providing a summary of the basic information available on medical registries and to highlight the progress and potential applications in this field.
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Affiliation(s)
- Bogdan Pop
- Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania.,Department of Pathology, "Prof. Dr. Ion Chiricuta" Oncology Institute, Cluj-Napoca, Romania
| | - Bogdan Fetica
- Department of Pathology, "Prof. Dr. Ion Chiricuta" Oncology Institute, Cluj-Napoca, Romania
| | - Mihaiela Luminita Blaga
- Department of Information Technology, "Prof. Dr. Ion Chiricuta" Oncology Institute, Cluj-Napoca Cluj-Napoca, Romania
| | - Adrian Pavel Trifa
- Department of Medical Genetics, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania.,Department of Genetic Explorations, "Prof. Dr. Ion Chiricuta" Oncology Institute, Cluj-Napoca Cluj-Napoca, Romania
| | - Patriciu Achimas-Cadariu
- Department of Surgical and Gynecological Oncology, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania.,Department of Surgery, "Prof. Dr. Ion Chiricuta" Oncology Institute, Cluj-Napoca, Romania
| | - Catalin Ioan Vlad
- Department of Surgical and Gynecological Oncology, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania.,Department of Surgery, "Prof. Dr. Ion Chiricuta" Oncology Institute, Cluj-Napoca, Romania
| | - Andrei Achimas-Cadariu
- Department of Medical Informatics and Biostatistics, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania
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Dhombres F, Charlet J. As Ontologies Reach Maturity, Artificial Intelligence Starts Being Fully Efficient: Findings from the Section on Knowledge Representation and Management for the Yearbook 2018. Yearb Med Inform 2018; 27:140-145. [PMID: 30157517 PMCID: PMC6115232 DOI: 10.1055/s-0038-1667078] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Objectives:
To select, present, and summarize the best papers published in 2017 in the field of Knowledge Representation and Management (KRM).
Methods:
A comprehensive and standardized review of the medical informatics literature was performed to select the most interesting papers of KRM published in 2017, based on a PubMed query.
Results:
In direct line with the research on data integration presented in the KRM section of the 2017 edition of the International Medical Informatics Association (IMIA) Yearbook, the five best papers for 2018 demonstrate even further the added-value of ontology-based integration approaches for phenotype-genotype association mining. Additionally, among the 15 preselected papers, two aspects of KRM are in the spotlight: the design of knowledge bases and new challenges in using ontologies.
Conclusions:
Ontologies are demonstrating their maturity to integrate medical data and begin to support clinical practices. New challenges have emerged: the query on distributed semantically annotated datasets, the efficiency of semantic annotation processes, the semantic representation of large textual datasets, the control of biases associated with semantic annotations, and the computation of Bayesian indicators on data annotated with ontologies.
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Affiliation(s)
- Ferdinand Dhombres
- Sorbonne Université, Université Paris 13, Sorbonne Paris Cité, INSERM, UMR_S 1142, LIMICS, Paris, France.,Sorbonne Université Médecine, Service de Médecine Foetale, AP-HP/HUEP, Hôpital Armand Trousseau, Paris, France
| | - Jean Charlet
- Sorbonne Université, Université Paris 13, Sorbonne Paris Cité, INSERM, UMR_S 1142, LIMICS, Paris, France.,AP-HP, DRCI, Paris, France
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