SPAR: a random forest-based predictor for self-interacting proteins with fine-grained domain information
文献类型：期刊论文
| 作者 | Liu, Xuhan1; Yang, Shiping1; Li, Chen2,3; Zhang, Ziding1; Song, Jiangning2,3,4,5,6 |
| 刊名 | AMINO ACIDS
 |
| 出版日期 | 2016-07-01 |
| 卷号 | 48期号:7页码:1655-1665 |
| 关键词 | Self-interacting protein Prediction Machine learning Feature selection Domain-domain interaction |
| 英文摘要 | Protein self-interaction, i.e. the interaction between two or more identical proteins expressed by one gene, plays an important role in the regulation of cellular functions. Considering the limitations of experimental self-interaction identification, it is necessary to design specific bioinformatics tools for self-interacting protein (SIP) prediction from protein sequence information. In this study, we proposed an improved computational approach for SIP prediction, termed SPAR (Self-interacting Protein Analysis serveR). Firstly, we developed an improved encoding scheme named critical residues substitution (CRS), in which the fine-grained domain-domain interaction information was taken into account. Then, by employing the Random Forest algorithm, the performance of CRS was evaluated and compared with several other encoding schemes commonly used for sequence-based protein-protein interaction prediction. Through the tenfold cross-validation tests on a balanced training dataset, CRS performed the best, with the average accuracy up to 72.01 %. We further integrated CRS with other encoding schemes and identified the most important features using the mRMR (the minimum redundancy maximum relevance) feature selection method. Our SPAR model with selected features achieved an average accuracy of 92.09 % on the human-independent test set (the ratio of positives to negatives was about 1:11). Besides, we also evaluated the performance of SPAR on an independent yeast test set (the ratio of positives to negatives was about 1:8) and obtained an average accuracy of 76.96 %. The results demonstrate that SPAR is capable of achieving a reasonable performance in cross-species application. The SPAR server is freely available for academic use at http://systbio.cau.edu.cn/zzdlab/spar/. |
| WOS标题词 | Science & Technology ; Life Sciences & Biomedicine |
| 类目[WOS] | Biochemistry & Molecular Biology |
| 研究领域[WOS] | Biochemistry & Molecular Biology |
| 关键词[WOS] | INTERACTION NETWORKS ; WEB SERVER ; DATABASE ; SEQUENCE ; UPDATE ; SIMILARITY ; CURATION ; BIOLOGY ; DIMER |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000377409900011 |
| 源URL | [http://124.16.173.210/handle/834782/2900]  |
| 专题 | 天津工业生物技术研究所_结构生物信息学和整合系统生物学实验室 宋江宁_期刊论文 |
| 作者单位 | 1.China Agr Univ, Coll Biol Sci, State Key Lab Agrobiotechnol, Beijing 100193, Peoples R China 2.Monash Univ, Biomed Discovery Inst, Infect & Immun Program, Melbourne, Vic 3800, Australia 3.Monash Univ, Dept Biochem & Mol Biol, Melbourne, Vic 3800, Australia 4.Monash Univ, Fac Informat Technol, Monash Ctr Data Sci, Melbourne, Vic 3800, Australia 5.Chinese Acad Sci, Tianjin Inst Ind Biotechnol, Natl Engn Lab Ind Enzymes, Tianjin 300308, Peoples R China 6.Chinese Acad Sci, Tianjin Inst Ind Biotechnol, Key Lab Syst Microbial Biotechnol, Tianjin 300308, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Xuhan,Yang, Shiping,Li, Chen,et al. SPAR: a random forest-based predictor for self-interacting proteins with fine-grained domain information[J]. AMINO ACIDS,2016,48(7):1655-1665. |
| APA | Liu, Xuhan,Yang, Shiping,Li, Chen,Zhang, Ziding,&Song, Jiangning.(2016).SPAR: a random forest-based predictor for self-interacting proteins with fine-grained domain information.AMINO ACIDS,48(7),1655-1665. |
| MLA | Liu, Xuhan,et al."SPAR: a random forest-based predictor for self-interacting proteins with fine-grained domain information".AMINO ACIDS 48.7(2016):1655-1665. |
入库方式： OAI收割
来源：天津工业生物技术研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。