NeBcon: protein contact map prediction using neural network training coupled with naiive Bayes classifiers
文献类型：期刊论文
| 作者 | Wang, YT ; Zhang, Y; Zhang, Y (reprint author), Univ Michigan, Dept Computat Med & Bioinformat, Ann Arbor, MI 48109 USA.; Zhang, Y (reprint author), Univ Michigan, Dept Biol Chem, Ann Arbor, MI 48109 USA.; He, BJ; Mortuza, SM; Shen, HB
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| 刊名 | BIOINFORMATICS
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| 出版日期 | 2017 |
| 卷号 | 33期号:15页码:2296-2306 |
| DOI | http://dx.doi.org/10.1093/bioinformatics/btx164 |
| 英文摘要 | Motivation: 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. Results: We developed a new pipeline, NeBcon, which uses the naiive 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. |
| 学科主题 | Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Computer Science ; Mathematical & Computational Biology ; Mathematics |
| 语种 | 英语 |
| 源URL | [http://ir.itp.ac.cn/handle/311006/22000]  |
| 专题 | 理论物理研究所_理论物理所1978-2010年知识产出 |
| 通讯作者 | Zhang, Y (reprint author), Univ Michigan, Dept Computat Med & Bioinformat, Ann Arbor, MI 48109 USA.; Zhang, Y (reprint author), Univ Michigan, Dept Biol Chem, Ann Arbor, MI 48109 USA. |
| 推荐引用方式 GB/T 7714 | Wang, YT,Zhang, Y,Zhang, Y ,et al. NeBcon: protein contact map prediction using neural network training coupled with naiive Bayes classifiers[J]. BIOINFORMATICS,2017,33(15):2296-2306. |
| APA | Wang, YT.,Zhang, Y.,Zhang, Y .,Zhang, Y .,He, BJ.,...&Shen, HB.(2017).NeBcon: protein contact map prediction using neural network training coupled with naiive Bayes classifiers.BIOINFORMATICS,33(15),2296-2306. |
| MLA | Wang, YT,et al."NeBcon: protein contact map prediction using neural network training coupled with naiive Bayes classifiers".BIOINFORMATICS 33.15(2017):2296-2306. |
入库方式： OAI收割
来源：理论物理研究所
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