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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 ]
2019
Source PublicationSCIENTIFIC REPORTS
ISSN2045-2322
Volume9Issue:7Pages:1-12
Abstract

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.

DOI10.1038/s41598-019-46369-4
Indexed BySCI
WOS IDWOS:000474335800045
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Document Type期刊论文
Identifierhttp://ir.xjipc.cas.cn/handle/365002/7097
Collection多语种信息技术研究室
Affiliation1.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
Recommended Citation
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.
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