Convolutional neural network architectures for predicting DNA-protein binding.

ABSTRACT:

Motivation

Convolutional neural networks (CNN) have outperformed conventional methods in modeling the sequence specificity of DNA-protein binding. Yet inappropriate CNN architectures can yield poorer performance than simpler models. Thus an in-depth understanding of how to match CNN architecture to a given task is needed to fully harness the power of CNNs for computational biology applications.

Results

We present a systematic exploration of CNN architectures for predicting DNA sequence binding using a large compendium of transcription factor datasets. We identify the best-performing architectures by varying CNN width, depth and pooling designs. We find that adding convolutional kernels to a network is important for motif-based tasks. We show the benefits of CNNs in learning

SUBMITTER: Zeng H 

PROVIDER: S-EPMC4908339 | biostudies-literature | 2016 Jun

REPOSITORIES: biostudies-literature