Using pattern structures for analyzing ontology-based annotations of biomedical data

National audienceAnnotating data with concepts of an ontology is a common practice in the biomedical domain. Resulting annotations, i.e., data-concept relationships, are useful for data integration whereas the reference ontology can guide the analysis of integrated data. Then the analysis of annotations can provide relevant knowledge units to consider for extracting and understanding possible cor- relations between data. Formal Concept Analysis (FCA) which builds from a binary context a concept lattice can be used for such a knowledge discovery task. However annotated biomedical data are usually not binary and a scaling procedure for using FCA is required as a prepro- cessing, leading to problems of expressivity, ranging from loss of information to the generation of a large num- ber of additional binary attributes. By contrast, pattern structures o er a general FCA-based framework for buil- ding a concept lattice from complex data, e.g., a set of objects with partially ordered descriptions. In this pa- per, we show how to instantiate this general framework when descriptions are ordered by an ontology. We illus- trate our approach with the analysis of annotations of drug related documents, and we show the capabilities of the approach for knowledge discovery

Extraction d'association d'EIM à partir de dossiers patients : expérimentation avec les structures de patrons et les ontologies

National audienceLes Dossiers Médicaux Electroniques (DME) constituent une ressource de grand intérêt pour étudier les Evènements Indésirables Médicamenteux (EIM). Nous proposons ici de fouiller les DME pour identifier des EIM fréquemment associés dans des sous-groupes de patients. Les EIM ayant des manifestations complexes, nous utilisons l'analyse formelle de concepts et ses structures de patrons, un cadre mathématique permettant la généralisation, en exploitant les connaissances du domaine médical formalisées dans des ontologies. Les résultats obtenus dans trois expériences montrent que cette approche est flexible et permet d'extraire des règles d'association à divers niveaux de généralisation

Discovering ADE associations from EHRs using pattern structures and ontologies

International audiencePatient Electronic Health Records (EHRs) constitute an essential resource for studying Adverse Drug Events (ADEs). We explore an original approach to identify frequently associated ADEs in subgroups of patients. Because ADEs have complex manifestations, we use formal concept analysis and its pattern structures, a mathematical framework that allows generalization, while taking into account domain knowledge formalized in medical ontologies. Results obtained with three different settings show that this approach is flexible and allows extraction of association rules at various levels of generalization

Discovering and Comparing Relational Knowledge, the Example of Pharmacogenomics

Article in Proceedings of the EKAW Doctoral Consortium 2018 co-located with the 21st International Conference on Knowledge Engineering and Knowledge Management (EKAW 2018)Pharmacogenomics (PGx) studies the influence of the genome in drug response, with knowledge units of the form of ternary relationships genomic variation-drug-phenotype. State-of-the-art PGx knowledge is available in the biomedical literature as well as in specialized knowledge bases. Additionally, Electronic Health Records of hospitals can be mined to discover such knowledge units that can then be compared with the state of the art, in order to confirm or temper relationships lacking validation or clinical counterpart. However, both discovering and comparing PGx relationships face multiple challenges: heterogeneous descriptions of knowledge units (languages, vocabularies and granularities), missing values and importance of the time dimension. In this research, we aim at proposing a framework based on Semantic Web technologies and Formal Concept Analysis to discover, represent and compare PGx knowledge units. We present the first results, consisting of creating an integrated knowledge base of PGx knowledge units from various sources and defining comparison methods, as well as the remaining issues to tackle

An Approach Towards Classifying and Navigating RDF data based on Pattern Structures

International audienceWith an increased interest in machine processable data, more and more data is now published in RDF (Resource Description Framework) format. This RDF data is present in independent and distributed resources which needs to be centralized, navigated and searched for domain specific applications. This paper proposes a new approach based on Formal Concept Analysis (FCA) to create a navigation space over semantic web data. This approach uses an extension of FCA and takes RDF triples and RDF Schema present on several independent sources and provide centralized access over the data resulting from several resources. Afterwards, SPARQL queries can be posed over this navigation space to access these distributed resources from one platform for information retrieval purposes

Interactive Exploration over RDF Data using Formal Concept Analysis

International audienceWith an increased interest in machine processable data, many datasets are now published in RDF (Resource Description Framework) format in Linked Data Cloud. These data are distributed over independent resources which need to be centralized and explored for domain specific applications. This paper proposes a new approach based on interactive data exploration paradigm using Pattern Structures, an extension of Formal Concept Analysis, to provide exploration and navigation over Linked Data through concept lattices. It takes RDF triples and RDF Schema based on user requirements and provides one navigation space resulting from several RDF resources. This navigation space allows user to navigate and search only the part of data that is interesting for her

Using pattern structures for analyzing ontology-based annotations of biomedical data

National audienceAnnotating data with concepts of an ontology is a common practice in the biomedical domain. Resulting annotations, i.e., data-concept relationships, are useful for data integration whereas the reference ontology can guide the analysis of integrated data. Then the analysis of annotations can provide relevant knowledge units to consider for extracting and understanding possible cor- relations between data. Formal Concept Analysis (FCA) which builds from a binary context a concept lattice can be used for such a knowledge discovery task. However annotated biomedical data are usually not binary and a scaling procedure for using FCA is required as a prepro- cessing, leading to problems of expressivity, ranging from loss of information to the generation of a large num- ber of additional binary attributes. By contrast, pattern structures o er a general FCA-based framework for buil- ding a concept lattice from complex data, e.g., a set of objects with partially ordered descriptions. In this pa- per, we show how to instantiate this general framework when descriptions are ordered by an ontology. We illus- trate our approach with the analysis of annotations of drug related documents, and we show the capabilities of the approach for knowledge discovery

Workshop Notes of the Sixth International Workshop "What can FCA do for Artificial Intelligence?"

International audienc