Discovering epistatic feature interactions from neural network models of regulatory DNA sequences.

Journal: Bioinformatics (Oxford, England)
Published Date: Sep 1, 2018

Abstract

MOTIVATION: Transcription factors bind regulatory DNA sequences in a combinatorial manner to modulate gene expression. Deep neural networks (DNNs) can learn the cis-regulatory grammars encoded in regulatory DNA sequences associated with transcription factor binding and chromatin accessibility. Several feature attribution methods have been developed for estimating the predictive importance of individual features (nucleotides or motifs) in any input DNA sequence to its associated output prediction from a DNN model. However, these methods do not reveal higher-order feature interactions encoded by the models.

Authors

  • Peyton Greenside
    Biomedical Informatics Training Program, Stanford University, Stanford, CA.
  • Tyler Shimko
    Genetics, Stanford University, Stanford, CA.
  • Polly Fordyce
    Genetics, Stanford University, Stanford, CA.
  • Anshul Kundaje
    Department of Computer Science, Stanford University, Stanford, CA, USA.

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