{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,7]],"date-time":"2026-06-07T05:43:39Z","timestamp":1780811019133,"version":"3.54.1"},"reference-count":39,"publisher":"Oxford University Press (OUP)","issue":"15","license":[{"start":{"date-parts":[[2017,3,28]],"date-time":"2017-03-28T00:00:00Z","timestamp":1490659200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/about_us\/legal\/notices"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["ACI-1053575"],"award-info":[{"award-number":["ACI-1053575"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000057","name":"National Institute of General Medical Sciences","doi-asserted-by":"publisher","award":["GM083107"],"award-info":[{"award-number":["GM083107"]}],"id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000057","name":"National Institute of General Medical Sciences","doi-asserted-by":"publisher","award":["GM116960"],"award-info":[{"award-number":["GM116960"]}],"id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["DBI1564756"],"award-info":[{"award-number":["DBI1564756"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31628003"],"award-info":[{"award-number":["31628003"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["91227115"],"award-info":[{"award-number":["91227115"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,8,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Recent CASP experiments have witnessed exciting progress on folding large-size non-humongous proteins with the assistance of co-evolution based contact predictions. The success is however anecdotal due to the requirement of the contact prediction methods for the high volume of sequence homologs that are not available to most of the non-humongous protein targets. Development of efficient methods that can generate balanced and reliable contact maps for different type of protein targets is essential to enhance the success rate of the ab initio protein structure prediction.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We developed a new pipeline, NeBcon, which uses the na\u00efve Bayes classifier (NBC) theorem to combine eight state of the art contact methods that are built from co-evolution and machine learning approaches. The posterior probabilities of the NBC model are then trained with intrinsic structural features through neural network learning for the final contact map prediction. NeBcon was tested on 98 non-redundant proteins, which improves the accuracy of the best co-evolution based meta-server predictor by 22%; the magnitude of the improvement increases to 45% for the hard targets that lack sequence and structural homologs in the databases. Detailed data analysis showed that the major contribution to the improvement is due to the optimized NBC combination of the complementary information from both co-evolution and machine learning predictions. The neural network training also helps to improve the coupling of the NBC posterior probability and the intrinsic structural features, which were found particularly important for the proteins that do not have sufficient number of homologous sequences to derive reliable co-evolution profiles.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availiablity and Implementation<\/jats:title>\n                  <jats:p>On-line server and standalone package of the program are available at http:\/\/zhanglab.ccmb.med.umich.edu\/NeBcon\/.<\/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\/btx164","type":"journal-article","created":{"date-parts":[[2017,3,22]],"date-time":"2017-03-22T00:55:07Z","timestamp":1490144107000},"page":"2296-2306","source":"Crossref","is-referenced-by-count":77,"title":["NeBcon: protein contact map prediction using neural network training coupled with na\u00efve Bayes classifiers"],"prefix":"10.1093","volume":"33","author":[{"given":"Baoji","family":"He","sequence":"first","affiliation":[{"name":"Institute of Theoretical Physics, Chinese Academy of Sciences, Beijing, China"},{"name":"School of Physical Sciences, University of Chinese Academy of Sciences, Beijing, China"},{"name":"Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S M","family":"Mortuza","sequence":"additional","affiliation":[{"name":"Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanting","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Theoretical Physics, Chinese Academy of Sciences, Beijing, China"},{"name":"School of Physical Sciences, University of Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong-Bin","family":"Shen","sequence":"additional","affiliation":[{"name":"Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA"},{"name":"Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA"},{"name":"Department of Biological Chemistry, University of Michigan, Ann Arbor, MI, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2017,3,28]]},"reference":[{"key":"2023070101190162800_btx164-B1","doi-asserted-by":"crossref","first-page":"3389","DOI":"10.1093\/nar\/25.17.3389","article-title":"Gapped BLAST and PSI-BLAST: a new generation of protein database search programs","volume":"25","author":"Altschul","year":"1997","journal-title":"Nucleic Acids Res"},{"key":"2023070101190162800_btx164-B2","doi-asserted-by":"crossref","first-page":"e1000633","DOI":"10.1371\/journal.pcbi.1000633","article-title":"Disentangling direct from indirect co-evolution of residues in protein alignments","volume":"6","author":"Burger","year":"2010","journal-title":"PLoS Comput. 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