Comprehensive evaluation of deep learning architectures for prediction of DNA/RNA sequence binding specificities.

ABSTRACT:

Motivation

Deep learning architectures have recently demonstrated their power in predicting DNA- and RNA-binding specificity. Existing methods fall into three classes: Some are based on convolutional neural networks (CNNs), others use recurrent neural networks (RNNs) and others rely on hybrid architectures combining CNNs and RNNs. However, based on existing studies the relative merit of the various architectures remains unclear.

Results

In this study we present a systematic exploration of deep learning architectures for predicting DNA- and RNA-binding specificity. For this purpose, we present deepRAM, an end-to-end deep learning tool that provides an implementation of a wide selection of architectures; its fully automatic model selection procedure allows us to perform a fair

SUBMITTER: Trabelsi A 

PROVIDER: S-EPMC6612801 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature