Fouille de motifs séquentiels pour la découverte de relations entre gènes et maladies rares

National audienceOrphanet est un organisme dont l'objectif est notamment de rassembler des collections d'articles traitant de maladies rares. Cependant, l'acquisition de nouvelles connaissances dans ce domaine est actuellement réalisée manuellement. Dès lors, obtenir de nouvelles informations relatives aux maladies rares est un processus chronophage. Permettre d'obtenir ces informations de manière automatique est donc un enjeu important. Dans ce contexte, nous proposons d'aborder la question de l'extraction de relations entre gènes et maladies rares en utilisant des approches de fouille de données, plus particulièrement de fouille de motifs séquentiels sous contraintes. Nos expérimentations montrent l'intérêt de notre approche pour l'extraction de relations entre gènes et maladies rares à partir de résumés d'articles de PubMe

Sequential Patterns to Discover and Characterise Biological Relations

International audienceIn this paper, we present a method to automatically detect and characterise interactions between genes in biomedical literature. Our approach is based on a combination of data mining techniques: frequent sequential patterns filtered by linguistic constraints and recursive mining. Unlike most Natural Language Processing (NLP) approaches, our approach does not use syntactic parsing to learn and apply linguistic rules. It does not require any resource except the training corpus to learn patterns. The process is in two steps. First, frequent sequential patterns are extracted from the training corpus. Second, after validation of those patterns, they are applied on the application corpus to detect and characterise new interactions. An advantage of our method is that interactions can be enhanced with modalities and biological information. We use two corpora containing only sentences with gene interactions as training corpus. Another corpus from PubMed abstracts is used as application corpus. We conduct an evaluation that shows that the precision of our approach is good and the recall correct for both targets: interaction detection and interaction characterisation

Sequential Patterns to Discover and Characterise Biological Relations

International audienceIn this paper, we present a method to automatically detect and characterise interactions between genes in biomedical literature. Our approach is based on a combination of data mining techniques: frequent sequential patterns filtered by linguistic constraints and recursive mining. Unlike most Natural Language Processing (NLP) approaches, our approach does not use syntactic parsing to learn and apply linguistic rules. It does not require any resource except the training corpus to learn patterns. The process is in two steps. First, frequent sequential patterns are extracted from the training corpus. Second, after validation of those patterns, they are applied on the application corpus to detect and characterise new interactions. An advantage of our method is that interactions can be enhanced with modalities and biological information. We use two corpora containing only sentences with gene interactions as training corpus. Another corpus from PubMed abstracts is used as application corpus. We conduct an evaluation that shows that the precision of our approach is good and the recall correct for both targets: interaction detection and interaction characterisation