{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T13:23:17Z","timestamp":1771852997503,"version":"3.50.1"},"reference-count":26,"publisher":"World Scientific Pub Co Pte Ltd","issue":"05","funder":[{"name":"Personnel Training Program of Academic and Technical Leaders of Yunnan Province"},{"name":"Integration of Cloud Computing and Big Data, Innovation of Science and Education","award":["2017B00016"],"award-info":[{"award-number":["2017B00016"]}]},{"DOI":"10.13039\/501100001809","name":"National Science Foundation of China","doi-asserted-by":"crossref","award":["11661081"],"award-info":[{"award-number":["11661081"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Bioinform. Comput. Biol."],"published-print":{"date-parts":[[2018,10]]},"abstract":"<jats:p> Protein secondary structure prediction (PSSP) is an important research field in bioinformatics. The representation of protein sequence features could be treated as a matrix, which includes the amino-acid residue (time-step) dimension and the feature vector dimension. Common approaches to predict secondary structures only focus on the amino-acid residue dimension. However, the feature vector dimension may also contain useful information for PSSP. To integrate the information on both dimensions of the matrix, we propose a hybrid deep learning framework, two-dimensional convolutional bidirectional recurrent neural network (2C-BRNN), for improving the accuracy of 8-class secondary structure prediction. The proposed hybrid framework is to extract the discriminative local interactions between amino-acid residues by two-dimensional convolutional neural networks (2DCNNs), and then further capture long-range interactions between amino-acid residues by bidirectional gated recurrent units (BGRUs) or bidirectional long short-term memory (BLSTM). Specifically, our proposed 2C-BRNNs framework consists of four models: 2DConv-BGRUs, 2DCNN-BGRUs, 2DConv-BLSTM and 2DCNN-BLSTM. Among these four models, the 2DConv- models only contain two-dimensional (2D) convolution operations. Moreover, the 2DCNN- models contain 2D convolutional and pooling operations. Experiments are conducted on four public datasets. The experimental results show that our proposed 2DConv-BLSTM model performs significantly better than the benchmark models. Furthermore, the experiments also demonstrate that the proposed models can extract more meaningful features from the matrix of proteins, and the feature vector dimension is also useful for PSSP. The codes and datasets of our proposed methods are available at https:\/\/github.com\/guoyanb\/JBCB2018\/ . <\/jats:p>","DOI":"10.1142\/s021972001850021x","type":"journal-article","created":{"date-parts":[[2018,8,20]],"date-time":"2018-08-20T01:46:45Z","timestamp":1534729605000},"page":"1850021","source":"Crossref","is-referenced-by-count":61,"title":["Protein secondary structure prediction improved by recurrent neural networks integrated with two-dimensional convolutional neural networks"],"prefix":"10.1142","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9532-2309","authenticated-orcid":false,"given":"Yanbu","family":"Guo","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering, Yunnan University, No. 2 North Cuihu Road, Kunming 650091, P. R. China"}]},{"given":"Bingyi","family":"Wang","sequence":"additional","affiliation":[{"name":"The Research Institute of Resource Insects, Chinese Academy of Forestry, Bailongsi, Kunming 650224, P. R. China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9060-382X","authenticated-orcid":false,"given":"Weihua","family":"Li","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Yunnan University, No. 2 North Cuihu Road, Kunming 650091, P. R. China"}]},{"given":"Bei","family":"Yang","sequence":"additional","affiliation":[{"name":"MD. Cardiology Department, The Second People\u2019s Hospital of Yunnan Province, No. 176 Qingnian Road, Kunming 650021, P. R. China"}]}],"member":"219","published-online":{"date-parts":[[2018,11,12]]},"reference":[{"key":"S021972001850021XBIB001","doi-asserted-by":"publisher","DOI":"10.1109\/RBME.2008.2008239"},{"key":"S021972001850021XBIB002","doi-asserted-by":"publisher","DOI":"10.1006\/jmbi.1993.1413"},{"issue":"5","key":"S021972001850021XBIB003","first-page":"851","volume":"18","author":"Min S","year":"2017","journal-title":"Briefings Bioinf"},{"key":"S021972001850021XBIB004","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/25.17.3389"},{"key":"S021972001850021XBIB007","doi-asserted-by":"publisher","DOI":"10.1002\/minf.201501008"},{"key":"S021972001850021XBIB008","doi-asserted-by":"publisher","DOI":"10.15252\/msb.20156651"},{"key":"S021972001850021XBIB009","doi-asserted-by":"publisher","DOI":"10.1016\/0022-2836(88)90564-5"},{"key":"S021972001850021XBIB010","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkw306"},{"key":"S021972001850021XBIB011","doi-asserted-by":"publisher","DOI":"10.1038\/srep18962"},{"key":"S021972001850021XBIB012","doi-asserted-by":"publisher","DOI":"10.1002\/bip.360221211"},{"key":"S021972001850021XBIB013","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.37.11.729"},{"key":"S021972001850021XBIB014","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btx218"},{"key":"S021972001850021XBIB015","doi-asserted-by":"publisher","DOI":"10.1006\/jmbi.1999.3091"},{"key":"S021972001850021XBIB016","doi-asserted-by":"publisher","DOI":"10.1006\/jmbi.2001.4580"},{"key":"S021972001850021XBIB018","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0141287"},{"key":"S021972001850021XBIB020","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2491929"},{"key":"S021972001850021XBIB021","doi-asserted-by":"publisher","DOI":"10.1002\/prot.25487"},{"key":"S021972001850021XBIB022","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2105-7-S4-S14"},{"key":"S021972001850021XBIB023","doi-asserted-by":"publisher","DOI":"10.1109\/TASLP.2014.2383614"},{"key":"S021972001850021XBIB026","doi-asserted-by":"publisher","DOI":"10.1002\/prot.10082"},{"key":"S021972001850021XBIB027","doi-asserted-by":"publisher","DOI":"10.1021\/bi00699a002"},{"key":"S021972001850021XBIB028","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmgm.2017.07.015"},{"key":"S021972001850021XBIB029","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2491929"},{"key":"S021972001850021XBIB033","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btg224"},{"key":"S021972001850021XBIB034","doi-asserted-by":"publisher","DOI":"10.1002\/prot.24347"},{"key":"S021972001850021XBIB035","doi-asserted-by":"publisher","DOI":"10.1002\/prot.24452"}],"container-title":["Journal of Bioinformatics and Computational Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S021972001850021X","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,6]],"date-time":"2019-08-06T09:35:23Z","timestamp":1565084123000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S021972001850021X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,10]]},"references-count":26,"journal-issue":{"issue":"05","published-online":{"date-parts":[[2018,11,12]]},"published-print":{"date-parts":[[2018,10]]}},"alternative-id":["10.1142\/S021972001850021X"],"URL":"https:\/\/doi.org\/10.1142\/s021972001850021x","relation":{},"ISSN":["0219-7200","1757-6334"],"issn-type":[{"value":"0219-7200","type":"print"},{"value":"1757-6334","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,10]]}}}