Modeling positional effects of regulatory sequences with spline transformations increases prediction accuracy of deep neural networks.

ABSTRACT:

Motivation

Regulatory sequences are not solely defined by their nucleic acid sequence but also by their relative distances to genomic landmarks such as transcription start site, exon boundaries or polyadenylation site. Deep learning has become the approach of choice for modeling regulatory sequences because of its strength to learn complex sequence features. However, modeling relative distances to genomic landmarks in deep neural networks has not been addressed.

Results

Here we developed spline transformation, a neural network module based on splines to flexibly and robustly model distances. Modeling distances to various genomic landmarks with spline transformations significantly increased state-of-the-art prediction accuracy of in vivo RNA-binding protein binding sites for

SUBMITTER: Avsec Z 

PROVIDER: S-EPMC5905632 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature