Using Two-dimensional Principal Component Analysis and Rotation Forest for Prediction of Protein-Protein Interactions

Lei Wang, Zhu Hong You, Xin Yan, Shi Xiong Xia, Feng Liu, Li Ping Li, Wei Zhang, Yong Zhou

科研成果: 期刊稿件文章同行评审

38 引用 (Scopus)

摘要

The interaction among proteins is essential in all life activities, and it is the basis of all the metabolic activities of the cells. By studying the protein-protein interactions (PPIs), people can better interpret the function of protein, decoding the phenomenon of life, especially in the design of new drugs with great practical value. Although many high-throughput techniques have been devised for large-scale detection of PPIs, these methods are still expensive and time-consuming. For this reason, there is a much-needed to develop computational methods for predicting PPIs at the entire proteome scale. In this article, we propose a new approach to predict PPIs using Rotation Forest (RF) classifier combine with matrix-based protein sequence. We apply the Position-Specific Scoring Matrix (PSSM), which contains biological evolution information, to represent protein sequences and extract the features through the two-dimensional Principal Component Analysis (2DPCA) algorithm. The descriptors are then sending to the rotation forest classifier for classification. We obtained 97.43% prediction accuracy with 94.92% sensitivity at the precision of 99.93% when the proposed method was applied to the PPIs data of yeast. To evaluate the performance of the proposed method, we compared it with other methods in the same dataset, and validate it on an independent datasets. The results obtained show that the proposed method is an appropriate and promising method for predicting PPIs.

源语言英语
文章编号12874
期刊Scientific Reports
8
1
DOI
出版状态已出版 - 1 12月 2018
已对外发布

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