Convolutional neural network architectures for predicting DNA–protein binding

Zeng, Haoyang; Edwards, Matthew D.; Liu, Ge; Gifford, David K.

doi:10.1093/bioinformatics/btw255

Cited by 431 publications

(399 citation statements)

References 11 publications

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“…• Medical DNNs have played an important role in genomics to gain insight into the genetics of diseases such as autism, cancers, and spinal muscular atrophy [24][25][26][27]. They have also been used in medical imaging to detect skin cancer [5], brain cancer [28] and breast cancer [29].…”

Section: E Applications Of Dnnmentioning

confidence: 99%

Efficient Processing of Deep Neural Networks: A Tutorial and Survey

et al. 2017

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Abstract-Deep neural networks (DNNs) are currently widely used for many artificial intelligence (AI) applications including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Accordingly, techniques that enable efficient processing of DNNs to improve energy efficiency and throughput without sacrificing application accuracy or increasing hardware cost are critical to the wide deployment of DNNs in AI systems.This article aims to provide a comprehensive tutorial and survey about the recent advances towards the goal of enabling efficient processing of DNNs. Specifically, it will provide an overview of DNNs, discuss various hardware platforms and architectures that support DNNs, and highlight key trends in reducing the computation cost of DNNs either solely via hardware design changes or via joint hardware design and DNN algorithm changes. It will also summarize various development resources that enable researchers and practitioners to quickly get started in this field, and highlight important benchmarking metrics and design considerations that should be used for evaluating the rapidly growing number of DNN hardware designs, optionally including algorithmic co-designs, being proposed in academia and industry.The reader will take away the following concepts from this article: understand the key design considerations for DNNs; be able to evaluate different DNN hardware implementations with benchmarks and comparison metrics; understand the trade-offs between various hardware architectures and platforms; be able to evaluate the utility of various DNN design techniques for efficient processing; and understand recent implementation trends and opportunities.

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Section: E Applications Of Dnnmentioning

confidence: 99%

Efficient Processing of Deep Neural Networks: A Tutorial and Survey

et al. 2017

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“…In particular, we aimed to identify DNA sequences that could predict cell-type-specific effects of regulatory variants. We investigated the use of machine learning models to predict the chromatin activity of regulatory elements across our three cell types using DNA sequence only (Zhou and Troyanskaya 2015;Hashimoto et al 2016;Kelley et al 2016;Zeng et al 2016). We developed a four-layered neural network architecture, OrbWeaver, to predict cell-type-specific chromatin accessibility of 500-bp windows centered at a regulatory locus ( Fig.…”

Section: Sequence-based Model For Chromatin Activity Explains the Regmentioning

confidence: 99%

Impact of regulatory variation across human iPSCs and differentiated cells

et al. 2017

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Induced pluripotent stem cells (iPSCs) are an essential tool for studying cellular differentiation and cell types that are otherwise difficult to access. We investigated the use of iPSCs and iPSC-derived cells to study the impact of genetic variation on gene regulation across different cell types and as models for studies of complex disease. To do so, we established a panel of iPSCs from 58 well-studied Yoruba lymphoblastoid cell lines (LCLs); 14 of these lines were further differentiated into cardiomyocytes. We characterized regulatory variation across individuals and cell types by measuring gene expression levels, chromatin accessibility, and DNA methylation. Our analysis focused on a comparison of inter-individual regulatory variation across cell types. While most cell-type-specific regulatory quantitative trait loci (QTLs) lie in chromatin that is open only in the affected cell types, we found that 20% of cell-type-specific regulatory QTLs are in shared open chromatin. This observation motivated us to develop a deep neural network to predict open chromatin regions from DNA sequence alone. Using this approach, we were able to use the sequences of segregating haplotypes to predict the effects of common SNPs on cell-type-specific chromatin accessibility.

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“…Zeng et al [96] make experiments with various parameters and draw the conclusion that the improved CNN model excels in the performance by effectively adjusting the parameters, such as the number of the convolution layers, the number of the convolution filters and the length of the convolution filter window et al…”

Section: Deep Convolution Neural Network Improvementmentioning

confidence: 99%

Noncoding Variants Functional Prioritization Methods Based on Predicted Regulatory Factor Binding Sites

Yang²,

Zhang

2017

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Backgrounds: With the advent of the post genomic era, the research for the genetic mechanism of the diseases has found to be increasingly depended on the studies of the genes, the gene-networks and gene-protein interaction networks. To explore gene expression and regulation, the researchers have carried out many studies on transcription factors and their binding sites (TFBSs). Based on the large amount of transcription factor binding sites predicting values in the deep learning models, further computation and analysis have been done to reveal the relationship between the gene mutation and the occurrence of the disease. It has been demonstrated that based on the deep learning methods, the performances of the prediction for the functions of the noncoding variants are outperforming than those of the conventional methods. The research on the prediction for functions of Single Nucleotide Polymorphisms (SNPs) is expected to uncover the mechanism of the gene mutation affection on traits and diseases of human beings. Results: We reviewed the conventional TFBSs identification methods from different perspectives. As for the deep learning methods to predict the TFBSs, we discussed the related problems, such as the raw data preprocessing, the structure design of the deep convolution neural network (CNN) and the model performance measure et al. And then we summarized the techniques that usually used in finding out the functional noncoding variants from de novo sequence. Conclusion: Along with the rapid development of the high-throughout assays, more and more sample data and chromatin features would be conducive to improve the prediction accuracy of the deep convolution neural network for TFBSs identification. Meanwhile, getting more insights into the deep CNN framework itself has been proved useful for both the promotion on model performance and the development for more suitable design to sample data. Based on the feature values predicted by the deep CNN model, the prioritization model for functional noncoding variants would contribute to reveal the affection of gene mutation on the diseases.

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Convolutional neural network architectures for predicting DNA–protein binding

Cited by 431 publications

References 11 publications

Efficient Processing of Deep Neural Networks: A Tutorial and Survey

Efficient Processing of Deep Neural Networks: A Tutorial and Survey

Impact of regulatory variation across human iPSCs and differentiated cells

Noncoding Variants Functional Prioritization Methods Based on Predicted Regulatory Factor Binding Sites

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