標題: Protein-protein interaction prediction using a hybrid feature representation and a stacked generalization scheme
作者: Chen, Kuan-Hsi
Wang, Tsai-Feng
Hu, Yuh-Jyh
交大名義發表
分子醫學與生物工程研究所
資訊工程學系
National Chiao Tung University
Institute of Molecular Medicine and Bioengineering
Department of Computer Science
關鍵字: Protein-protein interaction;Stacked generalization;Gene ontology;Network topology
公開日期: 10-六月-2019
摘要: BackgroundAlthough various machine learning-based predictors have been developed for estimating protein-protein interactions, their performances vary with dataset and species, and are affected by two primary aspects: choice of learning algorithm, and the representation of protein pairs. To improve the performance of predicting protein-protein interactions, we exploit the synergy of multiple learning algorithms, and utilize the expressiveness of different protein-pair features.ResultsWe developed a stacked generalization scheme that integrates five learning algorithms. We also designed three types of protein-pair features based on the physicochemical properties of amino acids, gene ontology annotations, and interaction network topologies. When tested on 19 published datasets collected from eight species, the proposed approach achieved a significantly higher or comparable overall performance, compared with seven competitive predictors.ConclusionWe introduced an ensemble learning approach for PPI prediction that integrated multiple learning algorithms and different protein-pair representations. The extensive comparisons with other state-of-the-art prediction tools demonstrated the feasibility and superiority of the proposed method.
URI: http://dx.doi.org/10.1186/s12859-019-2907-1
http://hdl.handle.net/11536/152300
ISSN: 1471-2105
DOI: 10.1186/s12859-019-2907-1
期刊: BMC BIOINFORMATICS
Volume: 20
起始頁: 0
結束頁: 0
顯示於類別:期刊論文