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Supervised learning approaches for TFBS predictions require large amounts of labeled data. However, many TFs of certain cell types either do not have sufficient labeled data or do not have any labeled data.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>In this paper, a multi-task learning framework (called MTTFsite) is proposed to address the lack of labeled data problem by leveraging on labeled data available in cross-cell types. The proposed MTTFsite contains a shared CNN to learn common features for all cell types and a private CNN for each cell type to learn private features. The common features are aimed to help predicting TFBSs for all cell types especially those cell types that lack labeled data. MTTFsite is evaluated on 241 cell type TF pairs and compared with a baseline method without using any multi-task learning model and a fully shared multi-task model that uses only a shared CNN and do not use private CNNs. For cell types with insufficient labeled data, results show that MTTFsite performs better than the baseline method and the fully shared model on more than 89% pairs. For cell types without any labeled data, MTTFsite outperforms the baseline method and the fully shared model by more than 80 and 93% pairs, respectively. A novel gene expression prediction method (called TFChrome) using both MTTFsite and histone modification features is also presented. Results show that TFBSs predicted by MTTFsite alone can achieve good performance. When MTTFsite is combined with histone modification features, a significant 5.7% performance improvement is obtained.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The resource and executable code are freely available at http:\/\/hlt.hitsz.edu.cn\/MTTFsite\/ and http:\/\/www.hitsz-hlt.com:8080\/MTTFsite\/.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btz451","type":"journal-article","created":{"date-parts":[[2019,5,30]],"date-time":"2019-05-30T11:09:02Z","timestamp":1559214542000},"page":"5067-5077","source":"Crossref","is-referenced-by-count":29,"title":["MTTFsite: cross-cell type TF binding site prediction by using multi-task learning"],"prefix":"10.1093","volume":"35","author":[{"given":"Jiyun","family":"Zhou","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School , Shenzhen, China"},{"name":"Department of Computing, The Hong Kong Polytechnic University , Hung Hom, Hong Kong"}]},{"given":"Qin","family":"Lu","sequence":"additional","affiliation":[{"name":"Department of Computing, The Hong Kong Polytechnic University , Hung Hom, Hong Kong"}]},{"given":"Lin","family":"Gui","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Warwick , Coventry CV4 4AL, UK"}]},{"given":"Ruifeng","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School , Shenzhen, China"}]},{"given":"Yunfei","family":"Long","sequence":"additional","affiliation":[{"name":"Department of Computing, The Hong Kong Polytechnic University , Hung Hom, Hong Kong"}]},{"given":"Hongpeng","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School , Shenzhen, China"}]}],"member":"286","published-online":{"date-parts":[[2019,6,4]]},"reference":[{"key":"2023013108390577600_btz451-B1","first-page":"831","author":"Alipanahi","year":"2015"},{"key":"2023013108390577600_btz451-B2","doi-asserted-by":"crossref","first-page":"4071.","DOI":"10.1038\/s41598-017-03199-6","article-title":"Predicting conformational ensembles and genome-wide transcription factor binding sites from DNA sequences","volume":"7","author":"Andrabi","year":"2017","journal-title":"Sci. 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