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

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.

Results

We present a new method called Deep Feature Interaction Maps (DFIM) to efficiently estimate interactions between all pairs of features in any input DNA sequence. DFI

SUBMITTER: Greenside P 

PROVIDER: S-EPMC6129272 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature