Identifying Genetic Variant Combinations using Skypatterns

Hoang-Son Pham 1 Dominique Lavenier 1 Alexandre Termier 2
1 GenScale - Scalable, Optimized and Parallel Algorithms for Genomics
IRISA-D7 - GESTION DES DONNÉES ET DE LA CONNAISSANCE, Inria Rennes – Bretagne Atlantique
2 LACODAM - Large Scale Collaborative Data Mining
Inria Rennes – Bretagne Atlantique , IRISA-D7 - GESTION DES DONNÉES ET DE LA CONNAISSANCE
Abstract : Identifying variant combination association with disease is a bioinformatics challenge. This problem can be solved by discriminative pattern mining that use statistical function to evaluate the significance of individual biological patterns. There is a wide range of such measures. However, selecting an appropriate measure as well as a suitable threshold in some specific practical situations is a difficult task. In this article, we propose to use the skypattern technique which allows combinations of measures to be used to evaluate the importance of variant combinations without having to select a given measure and a fixed threshold. Experiments on several real variant datasets demonstrate that the skypattern method effectively identifies the risk variant combinations related to diseases.
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Communication dans un congrès
7th International Workshop on Biological Knowledge Discovery and Data Mining (Workshop BIOKDD '16 ), Sep 2016, Porto, Portugal. 2016, 〈http://www.dexa.org/biokdd2016〉. 〈10.1109/DEXA.2016.13〉
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Dernière modification le : mardi 16 janvier 2018 - 15:54:26

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Hoang-Son Pham, Dominique Lavenier, Alexandre Termier. Identifying Genetic Variant Combinations using Skypatterns. 7th International Workshop on Biological Knowledge Discovery and Data Mining (Workshop BIOKDD '16 ), Sep 2016, Porto, Portugal. 2016, 〈http://www.dexa.org/biokdd2016〉. 〈10.1109/DEXA.2016.13〉. 〈hal-01385614〉

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