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Identifying complex motifs in massive omics data with a variable-convolutional layer in deep neural network

By Jing-Yi Li, Shen Jin, Xin-Ming Tu, Yang Ding, Ge Gao

Posted 31 Dec 2018
bioRxiv DOI: 10.1101/508242

Motif identification is among the most common and essential computational tasks for bioinformatics and genomics. Here we proposed a novel convolutional layer for deep neural network, named Variable Convolutional (vConv) layer, for effective motif identification in high-throughput omics data by learning kernel length from data adaptively. Empirical evaluations on DNA-protein binding and DNase footprinting cases well demonstrated that vConv-based networks have superior performance to their convolutional counterparts regardless of model complexity. Meanwhile, vConv could be readily integrated into multi-layer neural networks as an "in-place replacement" of canonical convolutional layer. All source codes are freely available on GitHub for academic usage.

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