Convolutional neural network architectures for predicting DNA–protein binding
Name
Convolutional neural network architectures for predicting DNA–protein binding.pdf
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439.39 KB
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Checksum (MD5)
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Author(s)
Zeng, Haoyang
Edwards, Matthew D
Liu, Ge
Gifford, David K
Date Issued
June 2016
Journal
Bioinformatics
Publisher
Oxford University Press
Citation
Zeng, Haoyang et al. “Convolutional Neural Network Architectures for Predicting DNA–protein Binding.” Bioinformatics 32, 12 (June 2016): i121–i127 © 2016 The Authors
Version
Final published version
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 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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-NonCommercial 4.0 International
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DOI of Published Version
https://doi.org/10.1093/bioinformatics/btw255
