Predicting Protein-Protein Interactions from Matrix-Based Protein Sequence Using Convolution Neural Network and Feature-Selective Rotation Forest | |
Wang, L (Wang, Lei)[ 1,2 ]; Wang, HF (Wang, Hai-Feng)[ 1 ]; Liu, SR (Liu, San-Rong)[ 1 ]; Yan, X (Yan, Xin)[ 3 ]; Song, KJ (Song, Ke-Jian)[ 4 ] | |
刊名 | SCIENTIFIC REPORTS |
2019 | |
卷号 | 9期号:7页码:1-12 |
ISSN号 | 2045-2322 |
DOI | 10.1038/s41598-019-46369-4 |
英文摘要 | Protein is an essential component of the living organism. The prediction of protein-protein interactions (PPIs) has important implications for understanding the behavioral processes of life, preventing diseases, and developing new drugs. Although the development of high-throughput technology makes it possible to identify PPIs in large-scale biological experiments, it restricts the extensive use of experimental methods due to the constraints of time, cost, false positive rate and other conditions. Therefore, there is an urgent need for computational methods as a supplement to experimental methods to predict PPIs rapidly and accurately. In this paper, we propose a novel approach, namely CNN-FSRF, for predicting PPIs based on protein sequence by combining deep learning Convolution Neural Network (CNN) with Feature-Selective Rotation Forest (FSRF). The proposed method firstly converts the protein sequence into the Position-Specific Scoring Matrix (PSSM) containing biological evolution information, then uses CNN to objectively and efficiently extracts the deeply hidden features of the protein, and finally removes the redundant noise information by FSRF and gives the accurate prediction results. When performed on the PPIs datasets Yeast and Helicobacter pylori, CNN-FSRF achieved a prediction accuracy of 97.75% and 88.96%. To further evaluate the prediction performance, we compared CNN-FSRF with SVM and other existing methods. In addition, we also verified the performance of CNN-FSRF on independent datasets. Excellent experimental results indicate that CNN-FSRF can be used as a useful complement to biological experiments to identify protein interactions. |
WOS记录号 | WOS:000474335800045 |
内容类型 | 期刊论文 |
源URL | [http://ir.xjipc.cas.cn/handle/365002/7101] |
专题 | 新疆理化技术研究所_多语种信息技术研究室 |
作者单位 | 1.Zaozhuang Univ, Coll Informat Sci & Engn, Zaozhuang 277100, Shandong, Peoples R China 2.Chinese Acad Sci, Xinjiang Tech Inst Phys & Chem, Urumqi 830011, Peoples R China 3.Zaozhuang Univ, Sch Foreign Languages, Zaozhuang 277100, Shandong, Peoples R China 4.JiangXi Univ Sci & Technol, Sch Informat Engn, Ganzhou 341000, Jiangxi, Peoples R China |
推荐引用方式 GB/T 7714 | Wang, L ,Wang, HF ,Liu, SR ,et al. Predicting Protein-Protein Interactions from Matrix-Based Protein Sequence Using Convolution Neural Network and Feature-Selective Rotation Forest[J]. SCIENTIFIC REPORTS,2019,9(7):1-12. |
APA | Wang, L ,Wang, HF ,Liu, SR ,Yan, X ,&Song, KJ .(2019).Predicting Protein-Protein Interactions from Matrix-Based Protein Sequence Using Convolution Neural Network and Feature-Selective Rotation Forest.SCIENTIFIC REPORTS,9(7),1-12. |
MLA | Wang, L ,et al."Predicting Protein-Protein Interactions from Matrix-Based Protein Sequence Using Convolution Neural Network and Feature-Selective Rotation Forest".SCIENTIFIC REPORTS 9.7(2019):1-12. |
个性服务 |
查看访问统计 |
相关权益政策 |
暂无数据 |
收藏/分享 |
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。
修改评论