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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 \u2018in-place replacement\u2019 of canonical convolutional layer. All source codes are freely available on GitHub for academic usage.<\/jats:p>","DOI":"10.1093\/bib\/bbab233","type":"journal-article","created":{"date-parts":[[2021,5,28]],"date-time":"2021-05-28T15:19:56Z","timestamp":1622215196000},"source":"Crossref","is-referenced-by-count":7,"title":["Identifying complex motifs in massive omics data with a variable-convolutional layer in deep neural network"],"prefix":"10.1093","volume":"22","author":[{"given":"Jing-Yi","family":"Li","sequence":"first","affiliation":[{"name":"Biomedical Pioneering Innovation Center & Beijing Advanced Innovation Center for Genomics, Center for Bioinformatics, and State Key Laboratory of Protein and Plant Gene Research at School of Life Sciences, Peking University, Beijing 100871, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shen","family":"Jin","sequence":"additional","affiliation":[{"name":"Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin-Ming","family":"Tu","sequence":"additional","affiliation":[{"name":"Biomedical Pioneering Innovation Center & Beijing Advanced Innovation Center for Genomics, Center for Bioinformatics, and State Key Laboratory of Protein and Plant Gene Research at School of Life Sciences, Peking University, Beijing 100871, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Ding","sequence":"additional","affiliation":[{"name":"Biomedical Pioneering Innovation Center & Beijing Advanced Innovation Center for Genomics, Center for Bioinformatics, and State Key Laboratory of Protein and Plant Gene Research at School of Life Sciences, Peking University, Beijing 100871, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6470-8815","authenticated-orcid":false,"given":"Ge","family":"Gao","sequence":"additional","affiliation":[{"name":"Biomedical Pioneering Innovation Center & Beijing Advanced Innovation Center for Genomics, Center for Bioinformatics, and State Key Laboratory of Protein and Plant Gene Research at School of Life Sciences, Peking University, Beijing 100871, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2021,7,2]]},"reference":[{"key":"2021110815062766000_ref1","doi-asserted-by":"crossref","DOI":"10.1186\/s13062-015-0090-5","article-title":"RNA motif discovery: a computational overview","volume":"10","author":"Achar","year":"2015","journal-title":"Biol Direct"},{"key":"2021110815062766000_ref2","first-page":"135","article-title":"DNA sequence motif: a jack of all trades for ChIP-Seq data. 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