Convolutional neural network architectures for predicting DNA-protein binding

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 tas...

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Bibliographic Details
Published in:Bioinformatics 2016-06, Vol.32 (12), p.i121-i127
Main Authors: Zeng, Haoyang, Edwards, Matthew D, Liu, Ge, Gifford, David K
Format: Article
Language:English
Subjects:
Algorithms
Architecture
Architecture (computers)
Binding
Bioinformatics
data collection
DNA
Mathematical models
Networks
Neural networks
Neural Networks (Computer)
nucleotide sequences
prediction
Protein Binding
Proteins
Tasks
transcription factors
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Summary: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. 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 rich higher-order sequence features, such as secondary motifs and local sequence context, by comparing network performance on multiple modeling tasks ranging in difficulty. We also demonstrate how careful construction of sequence benchmark datasets, using approaches that control potentially confounding effects like positional or motif strength bias, is critical in making fair comparisons between competing methods. We explore how to establish the sufficiency of training data for these learning tasks, and we have created a flexible cloud-based framework that permits the rapid exploration of alternative neural network architectures for problems in computational biology. All the models analyzed are available at http://cnn.csail.mit.edu gifford@mit.edu Supplementary data are available at Bioinformatics online.
ISSN:1367-4803
1460-2059
1367-4811
1460-2059
DOI:10.1093/bioinformatics/btw255