Visualizing complex feature interactions and feature sharing in genomic deep neural networks.

ABSTRACT:

Background

Visualization tools for deep learning models typically focus on discovering key input features without considering how such low level features are combined in intermediate layers to make decisions. Moreover, many of these methods examine a network's response to specific input examples that may be insufficient to reveal the complexity of model decision making.

Results

We present DeepResolve, an analysis framework for deep convolutional models of genome function that visualizes how input features contribute individually and combinatorially to network decisions. Unlike other methods, DeepResolve does not depend upon the analysis of a predefined set of inputs. Rather, it uses gradient ascent to stochastically explore intermediate feature maps to 1) discover important

SUBMITTER: Liu G 

PROVIDER: S-EPMC6642501 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature