AttOmics: attention-based architecture for diagnosis and prognosis from omics data.

Journal: Bioinformatics (Oxford, England)
Published Date:

Abstract

MOTIVATION: The increasing availability of high-throughput omics data allows for considering a new medicine centered on individual patients. Precision medicine relies on exploiting these high-throughput data with machine-learning models, especially the ones based on deep-learning approaches, to improve diagnosis. Due to the high-dimensional small-sample nature of omics data, current deep-learning models end up with many parameters and have to be fitted with a limited training set. Furthermore, interactions between molecular entities inside an omics profile are not patient specific but are the same for all patients.

Authors

  • Aurélien Beaude
    IBISC, Université Paris-Saclay, Univ Evry, 23 Boulevard de France, Evry-Courcouronnes 91020, France.
  • Milad Rafiee Vahid
    Sanofi R&D Data and Data Science, Artificial Intelligence & Deep Analytics, Omics Data Science, 450 Water Street, Cambridge, MA 02142, United States.
  • Franck Augé
    Artificial Intelligence & Deep Analytics, Omics Data Science, Sanofi R&D Data and Data Science, 1 Av. Pierre Brossolette, Chilly-Mazarin 91385, France.
  • Farida Zehraoui
    IBISC - IBGBI, University of Evry, 91037 Evry CEDEX, France.
  • Blaise Hanczar
    IBISC, Univ Evry, Université Paris-Saclay, 23 boulevard de France, 91034, Evry, France. blaise.hanczar@ibisc.univ-evry.fr.