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Highly Accurate Prediction of Protein-Protein Interactions via Incorporating Evolutionary Information and Physicochemical Characteristics

文献类型：期刊论文

作者Li, Zheng-Wei1; You, Zhu-Hong2; Chen, Xing3; Gui, Jie4; Nie, Ru1
刊名INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
出版日期2016-09-01
卷号17期号:9页码:1-12
关键词Evolutionary Information Physicochemical Characteristics Protein Sequence Protein Interactions Discriminative Vector Machine
DOI10.3390/ijms17091396
文献子类Article
英文摘要Protein-protein interactions (PPIs) occur at almost all levels of cell functions and play crucial roles in various cellular processes. Thus, identification of PPIs is critical for deciphering the molecular mechanisms and further providing insight into biological processes. Although a variety of high-throughput experimental techniques have been developed to identify PPIs, existing PPI pairs by experimental approaches only cover a small fraction of the whole PPI networks, and further, those approaches hold inherent disadvantages, such as being time-consuming, expensive, and having high false positive rate. Therefore, it is urgent and imperative to develop automatic in silico approaches to predict PPIs efficiently and accurately. In this article, we propose a novel mixture of physicochemical and evolutionary-based feature extraction method for predicting PPIs using our newly developed discriminative vector machine (DVM) classifier. The improvements of the proposed method mainly consist in introducing an effective feature extraction method that can capture discriminative features from the evolutionary-based information and physicochemical characteristics, and then a powerful and robust DVM classifier is employed. To the best of our knowledge, it is the first time that DVM model is applied to the field of bioinformatics. When applying the proposed method to the Yeast and Helicobacter pylori (H. pylori) datasets, we obtain excellent prediction accuracies of 94.35% and 90.61%, respectively. The computational results indicate that our method is effective and robust for predicting PPIs, and can be taken as a useful supplementary tool to the traditional experimental methods for future proteomics research.
WOS关键词WEIGHTED SPARSE REPRESENTATION ; FOLD RECOGNITION ; SEQUENCES ; HYPERPLANES ; COMPLEXES ; ALGORITHM ; DATABASES ; ENSEMBLE ; FUSION ; TOOL
WOS研究方向Biochemistry & Molecular Biology ; Chemistry
语种英语
WOS记录号WOS:000385525500024
资助机构National Science Foundation of China(61373086 ; National Science Foundation of China(61373086 ; National Science Foundation of China(61373086 ; National Science Foundation of China(61373086 ; Guangdong Natural Science Foundation(2014A030313555) ; Guangdong Natural Science Foundation(2014A030313555) ; Guangdong Natural Science Foundation(2014A030313555) ; Guangdong Natural Science Foundation(2014A030313555) ; Chinese Academy of Sciences ; Chinese Academy of Sciences ; Chinese Academy of Sciences ; Chinese Academy of Sciences ; CCF-Tencent Open Fund ; CCF-Tencent Open Fund ; CCF-Tencent Open Fund ; CCF-Tencent Open Fund ; 11301517 ; 11301517 ; 11301517 ; 11301517 ; 61572463) ; 61572463) ; 61572463) ; 61572463) ; National Science Foundation of China(61373086 ; National Science Foundation of China(61373086 ; National Science Foundation of China(61373086 ; National Science Foundation of China(61373086 ; Guangdong Natural Science Foundation(2014A030313555) ; Guangdong Natural Science Foundation(2014A030313555) ; Guangdong Natural Science Foundation(2014A030313555) ; Guangdong Natural Science Foundation(2014A030313555) ; Chinese Academy of Sciences ; Chinese Academy of Sciences ; Chinese Academy of Sciences ; Chinese Academy of Sciences ; CCF-Tencent Open Fund ; CCF-Tencent Open Fund ; CCF-Tencent Open Fund ; CCF-Tencent Open Fund ; 11301517 ; 11301517 ; 11301517 ; 11301517 ; 61572463) ; 61572463) ; 61572463) ; 61572463)
源URL[http://ir.hfcas.ac.cn:8080/handle/334002/30797]  
专题合肥物质科学研究院_中科院合肥智能机械研究所
作者单位1.China Univ Min & Technol, Sch Comp Sci & Technol, Xuzhou 21116, Peoples R China
2.Chinese Acad Sci, Xinjiang Tech Inst Phys & Chem, Urumqi 830011, Peoples R China
3.China Univ Min & Technol, Sch Informat & Elect Engn, Xuzhou 21116, Peoples R China
4.Chinese Acad Sci, Inst Intelligent Machines, Hefei 230031, Peoples R China
推荐引用方式
GB/T 7714		 Li, Zheng-Wei,You, Zhu-Hong,Chen, Xing,et al. Highly Accurate Prediction of Protein-Protein Interactions via Incorporating Evolutionary Information and Physicochemical Characteristics[J]. INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES,2016,17(9):1-12.
APA Li, Zheng-Wei,You, Zhu-Hong,Chen, Xing,Gui, Jie,&Nie, Ru.(2016).Highly Accurate Prediction of Protein-Protein Interactions via Incorporating Evolutionary Information and Physicochemical Characteristics.INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES,17(9),1-12.
MLA Li, Zheng-Wei,et al."Highly Accurate Prediction of Protein-Protein Interactions via Incorporating Evolutionary Information and Physicochemical Characteristics".INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES 17.9(2016):1-12.

入库方式： OAI收割

来源：合肥物质科学研究院

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