Predicting rrna-, rna-, and dna-binding proteins from primary structure with support vector machines
文献类型：期刊论文
| 作者 | Yu, XJ; Cao, JP; Cai, YD; Shi, TL; Li, YX |
| 刊名 | Journal of theoretical biology
 |
| 出版日期 | 2006-05-21 |
| 卷号 | 240期号:2页码:175-184 |
| 关键词 | Rrna-binding protein Rna-binding protein Dna-binding protein Protein function prediction Support vector machines (svms) |
| ISSN号 | 0022-5193 |
| DOI | 10.1016/j.jtbi.2005.09.018 |
| 通讯作者 | Cai, yd(yxli@sibs.ac.cn) |
| 英文摘要 | In the post-genome era, the prediction of protein function is one of the most demanding tasks in the study of bioinformatics. machine learning methods, such as the support vector machines (svms), greatly help to improve the classification of protein function. in this work, we integrated svms, protein sequence amino acid composition, and associated physicochemical properties into the study of nucleic-acid-binding proteins prediction. we developed the binary classifications for rrna-, rna-, dna-binding proteins that play an important role in the control of many cell processes. each svm predicts whether a protein belongs to rrna-, rna-, or dna-binding protein class. self-consistency and jackknife tests were performed on the protein data sets in which the sequences identity was < 25%. test results show that the accuracies of rrna-, rna-, dna-binding svms predictions are similar to 84%, similar to 78%, similar to 72%, respectively. the predictions were also performed on the ambiguous and negative data set. the results demonstrate that the predicted scores of proteins in the ambiguous data set by rna- and dna-binding svm models were distributed around zero, while most proteins in the negative data set were predicted as negative scores by all three svms. the score distributions agree well with the prior knowledge of those proteins and show the effectiveness of sequence associated physicochemical properties in the protein function prediction. the software is available from the author upon request. (c) 2005 elsevier ltd. all rights reserved. |
| WOS关键词 | FUNCTIONAL DOMAIN COMPOSITION ; AMINO-ACID-COMPOSITION ; EXPRESSION DATA ; SWISS-PROT ; SEQUENCE ; CLASSIFICATION ; RECOGNITION ; GENOME ; SVM ; CONSERVATION |
| WOS研究方向 | Life Sciences & Biomedicine - Other Topics ; Mathematical & Computational Biology |
| WOS类目 | Biology ; Mathematical & Computational Biology |
| 语种 | 英语 |
| WOS记录号 | WOS:000237828200002 |
| 出版者 | ACADEMIC PRESS LTD ELSEVIER SCIENCE LTD |
| URI标识 | http://www.irgrid.ac.cn/handle/1471x/2379351 |
| 专题 | 中国科学院大学 |
| 通讯作者 | Cai, YD |
| 作者单位 | 1.UMIST, Dept Biomol Sci, Manchester M60 1QD, Lancs, England 2.Chinese Acad Sci, Grad Sch, Shanghai Inst Biol Sci, Bioinformat Ctr, Shanghai 200031, Peoples R China 3.Univ Elect Sci & Technol China, Sch Life Sci & Technol, Dept Biomed Engn, Chengdu 610054, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yu, XJ,Cao, JP,Cai, YD,et al. Predicting rrna-, rna-, and dna-binding proteins from primary structure with support vector machines[J]. Journal of theoretical biology,2006,240(2):175-184. |
| APA | Yu, XJ,Cao, JP,Cai, YD,Shi, TL,&Li, YX.(2006).Predicting rrna-, rna-, and dna-binding proteins from primary structure with support vector machines.Journal of theoretical biology,240(2),175-184. |
| MLA | Yu, XJ,et al."Predicting rrna-, rna-, and dna-binding proteins from primary structure with support vector machines".Journal of theoretical biology 240.2(2006):175-184. |
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来源：中国科学院大学
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