{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T09:02:51Z","timestamp":1778058171695,"version":"3.51.4"},"reference-count":25,"publisher":"World Scientific Pub Co Pte Ltd","issue":"06","funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61602280"],"award-info":[{"award-number":["61602280"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61473179"],"award-info":[{"award-number":["61473179"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"crossref","award":["17F17050"],"award-info":[{"award-number":["17F17050"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Bioinform. Comput. Biol."],"published-print":{"date-parts":[[2019,12]]},"abstract":"<jats:p> Molecular recognition features (MoRFs) usually act as \u201chub\u201d sites in the interaction networks of intrinsically disordered proteins (IDPs). Because an increasing number of serious diseases have been found to be associated with disordered proteins, identifying MoRFs has become increasingly important. In this study, we propose an ensemble learning strategy, named MoRFPred_en, to predict MoRFs from protein sequences. This approach combines four submodels that utilize different sequence-derived features for the prediction, including a multichannel one-dimensional convolutional neural network (CNN_1D multichannel) based model, two deep two-dimensional convolutional neural network (DCNN_2D) based models, and a support vector machine (SVM) based model. When compared with other methods on the same datasets, the MoRFPred_en approach produced better results than existing state-of-the-art MoRF prediction methods, achieving an AUC of 0.762 on the VALIDATION419 dataset, 0.795 on the TEST45 dataset, and 0.776 on the TEST49 dataset. Availability: http:\/\/vivace.bi.a.u-tokyo.ac.jp:8008\/fang\/MoRFPred_en.php . <\/jats:p>","DOI":"10.1142\/s0219720019400158","type":"journal-article","created":{"date-parts":[[2019,11,25]],"date-time":"2019-11-25T04:37:48Z","timestamp":1574656668000},"page":"1940015","source":"Crossref","is-referenced-by-count":12,"title":["MoRFPred_en: Sequence-based prediction of MoRFs using an ensemble learning strategy"],"prefix":"10.1142","volume":"17","author":[{"given":"Chun","family":"Fang","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Shandong University of Technology, Shandong 255049, P. R. China"}]},{"given":"Yoshitaka","family":"Moriwaki","sequence":"additional","affiliation":[{"name":"Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo 113-8657, Japan"}]},{"given":"Caihong","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Shandong University of Technology, Shandong 255049, P. R. China"}]},{"given":"Kentaro","family":"Shimizu","sequence":"additional","affiliation":[{"name":"Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo 113-8657, Japan"}]}],"member":"219","published-online":{"date-parts":[[2020,1,31]]},"reference":[{"key":"S0219720019400158BIB001","doi-asserted-by":"publisher","DOI":"10.1021\/cr500288y"},{"key":"S0219720019400158BIB002","first-page":"509","volume":"37","author":"Tompa P","year":"2012","journal-title":"Cell"},{"key":"S0219720019400158BIB003","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmb.2006.07.087"},{"key":"S0219720019400158BIB004","doi-asserted-by":"publisher","DOI":"10.1021\/pr0701411"},{"key":"S0219720019400158BIB005","first-page":"1","volume":"18","author":"Gang H","year":"2017","journal-title":"Int J Mol Sci"},{"key":"S0219720019400158BIB006","doi-asserted-by":"publisher","DOI":"10.1146\/annurev.biophys.37.032807.125924"},{"key":"S0219720019400158BIB007","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.0801353105"},{"key":"S0219720019400158BIB008","doi-asserted-by":"publisher","DOI":"10.1021\/bi050736e"},{"key":"S0219720019400158BIB009","doi-asserted-by":"publisher","DOI":"10.1021\/bi7012273"},{"key":"S0219720019400158BIB010","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bts209"},{"key":"S0219720019400158BIB011","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btp518"},{"key":"S0219720019400158BIB012","first-page":"799","volume":"4","author":"Tuo Z","year":"2012","journal-title":"J Biomol Struct Dyn"},{"key":"S0219720019400158BIB013","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4939-6406-2_12"},{"key":"S0219720019400158BIB014","doi-asserted-by":"publisher","DOI":"10.3390\/ijms11103725"},{"key":"S0219720019400158BIB015","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2105-14-300"},{"key":"S0219720019400158BIB016","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btv060"},{"key":"S0219720019400158BIB017","doi-asserted-by":"publisher","DOI":"10.1016\/j.jtbi.2017.10.015"},{"key":"S0219720019400158BIB018","doi-asserted-by":"publisher","DOI":"10.1142\/S0219720019500045"},{"key":"S0219720019400158BIB019","first-page":"253","volume-title":"Proc 8th Int Joint Conf Natural Language Processing","author":"Zhang Ye","year":"2015"},{"key":"S0219720019400158BIB020","doi-asserted-by":"publisher","DOI":"10.1016\/j.bbapap.2013.01.006"},{"key":"S0219720019400158BIB021","doi-asserted-by":"publisher","DOI":"10.1007\/s10044-006-0041-y"},{"key":"S0219720019400158BIB022","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkm998"},{"key":"S0219720019400158BIB023","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-009-9124-7"},{"key":"S0219720019400158BIB024","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2006.10.012"},{"key":"S0219720019400158BIB025","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/25.17.3389"}],"container-title":["Journal of Bioinformatics and Computational Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0219720019400158","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,2,5]],"date-time":"2020-02-05T03:44:53Z","timestamp":1580874293000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S0219720019400158"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12]]},"references-count":25,"journal-issue":{"issue":"06","published-print":{"date-parts":[[2019,12]]}},"alternative-id":["10.1142\/S0219720019400158"],"URL":"https:\/\/doi.org\/10.1142\/s0219720019400158","relation":{},"ISSN":["0219-7200","1757-6334"],"issn-type":[{"value":"0219-7200","type":"print"},{"value":"1757-6334","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,12]]}}}