Biological interpretation of deep neural network for phenotype prediction based on gene expression.

Journal: BMC bioinformatics
Published Date:

Abstract

BACKGROUND: The use of predictive gene signatures to assist clinical decision is becoming more and more important. Deep learning has a huge potential in the prediction of phenotype from gene expression profiles. However, neural networks are viewed as black boxes, where accurate predictions are provided without any explanation. The requirements for these models to become interpretable are increasing, especially in the medical field.

Authors

  • Blaise Hanczar
    IBISC, Univ Evry, Université Paris-Saclay, 23 boulevard de France, 91034, Evry, France. blaise.hanczar@ibisc.univ-evry.fr.
  • Farida Zehraoui
    IBISC - IBGBI, University of Evry, 91037 Evry CEDEX, France.
  • Tina Issa
    IBISC, Univ Evry, Université Paris-Saclay, 23 boulevard de France, 91034, Evry, France.
  • Mathieu Arles
    IBISC, Univ Evry, Université Paris-Saclay, 23 boulevard de France, 91034, Evry, France.