A new convolution model for effective bio-motif detection via rationally design the \"black box\"

Jin, S.; Li, J.; Ding, Y.; Gao, G.

doi:10.1101/508242

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bioRxiv · 10.1101/508242

A new convolution model for effective bio-motif detection via rationally design the \"black box\"

Jin, S.·Li, J.·Ding, Y.·Gao, G.

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Abstract

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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Jin, S., Li, J., Ding, Y., Gao, G.. 2018-12-31. A new convolution model for effective bio-motif detection via rationally design the \"black box\". https://doi.org/10.1101/508242

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