Sequential Pattern Mining to Discover Relations between Genes and Rare Diseases

Nicolas Béchet 1 Peggy Cellier 2 Thierry Charnois 3 Bruno Crémilleux 3 Marie-Christine Jaulent
1 EXPRESSION - Expressiveness in Human Centered Data/Media
UBS - Université de Bretagne Sud, IRISA-D6 - MEDIA ET INTERACTIONS
2 LIS - Logical Information Systems
IRISA-D7 - GESTION DES DONNÉES ET DE LA CONNAISSANCE
3 Equipe CODAG - Laboratoire GREYC - UMR6072
GREYC - Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen
Abstract : Orphanet provides an international web-based knowledge portal for rare diseases including a collection of review articles. However, reviews and literature monitoring are manual. Thus, new documentation about a rare disease is a time-consuming process and automatically discovering knowledge from a large collection of texts is a crucial issue. This context represents a strong motivation to address the problem of extracting gene-rare diseases relationships from texts. In this paper, we tackle this issue with a cross-fertilization of information extraction and data mining techniques (sequential pattern mining under constraints). Experiments show the interest of the method for the documentation of rare diseases.
Keywords : Internet data mining diseases health care information retrieval medical computing portals text analysis
Type de document :
Communication dans un congrès
25th IEEE International Symposium on Computer-Based Medical Systems (CBMS'12), Jun 2012, Porto, Italy. IEEE, p1-6, 2012, 〈10.1109/CBMS.2012.6266367〉
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https://hal.archives-ouvertes.fr/hal-01023713
Contributeur : Greyc Référent <>
Soumis le : mardi 15 juillet 2014 - 10:34:36
Dernière modification le : mardi 5 février 2019 - 12:12:41

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Nicolas Béchet, Peggy Cellier, Thierry Charnois, Bruno Crémilleux, Marie-Christine Jaulent. Sequential Pattern Mining to Discover Relations between Genes and Rare Diseases. 25th IEEE International Symposium on Computer-Based Medical Systems (CBMS'12), Jun 2012, Porto, Italy. IEEE, p1-6, 2012, 〈10.1109/CBMS.2012.6266367〉. 〈hal-01023713〉

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