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In recent years, an increasing number of deep learning (DL) based methods have been proposed for predicting TF binding sites (TFBSs) and achieved impressive prediction performance. However, these methods mainly focus on predicting the sequence specificity of TF-DNA binding, which is equivalent to a sequence-level binary classification task, and fail to identify motifs and TFBSs accurately. In this paper, we developed a fully convolutional network coupled with global average pooling (FCNA), which by contrast is equivalent to a nucleotide-level binary classification task, to roughly locate TFBSs and accurately identify motifs. Experimental results on human ChIP-seq datasets show that FCNA outperforms other competing methods significantly. Besides, we find that the regions located by FCNA can be used by motif discovery tools to further refine the prediction performance. Furthermore, we observe that FCNA can accurately identify TF-DNA binding motifs across different cell lines and infer indirect TF-DNA bindings.<\/jats:p>","DOI":"10.1093\/bib\/bbaa435","type":"journal-article","created":{"date-parts":[[2020,12,30]],"date-time":"2020-12-30T12:11:46Z","timestamp":1609330306000},"source":"Crossref","is-referenced-by-count":46,"title":["Locating transcription factor binding sites by fully convolutional neural network"],"prefix":"10.1093","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4232-7736","authenticated-orcid":false,"given":"Qinhu","family":"Zhang","sequence":"first","affiliation":[{"name":"Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Tongji University, Shanghai, China"}]},{"given":"Siguo","family":"Wang","sequence":"additional","affiliation":[{"name":"Computer Science and Technology, Tongji University, 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