A Novel Collaborative Filtering Model-Based Method for Identifying Essential Proteins

Frontiers in Genetics

Zhiping Chen¹  Linai Kuang³  Xin He³  Xianyou Zhu⁴  Camara Lancine⁵ 

[1] College of Computer Engineering and Applied Mathematics, Changsha University, Changsha, China;College of Computer Science and Technology, Hengyang Normal University, Hengyang, China;College of Computer, Xiangtan University, Xiangtan, China;Hunan Provincial Key Laboratory of Intelligent Information Processing and Application, Hengyang, China;The Social Sciences and Management University of Bamako, Bamako, Mali;

关键词: essential proteins;    collaborative filtering model;    PDI network;    data integration;    prediction model;   

DOI  :  10.3389/fgene.2021.763153

来源: DOAJ

【 摘 要 】

Considering that traditional biological experiments are expensive and time consuming, it is important to develop effective computational models to infer potential essential proteins. In this manuscript, a novel collaborative filtering model-based method called CFMM was proposed, in which, an updated protein–domain interaction (PDI) network was constructed first by applying collaborative filtering algorithm on the original PDI network, and then, through integrating topological features of PDI networks with biological features of proteins, a calculative method was designed to infer potential essential proteins based on an improved PageRank algorithm. The novelties of CFMM lie in construction of an updated PDI network, application of the commodity-customer-based collaborative filtering algorithm, and introduction of the calculation method based on an improved PageRank algorithm, which ensured that CFMM can be applied to predict essential proteins without relying entirely on known protein–domain associations. Simulation results showed that CFMM can achieve reliable prediction accuracies of 92.16, 83.14, 71.37, 63.87, 55.84, and 52.43% in the top 1, 5, 10, 15, 20, and 25% predicted candidate key proteins based on the DIP database, which are remarkably higher than 14 competitive state-of-the-art predictive models as a whole, and in addition, CFMM can achieve satisfactory predictive performances based on different databases with various evaluation measurements, which further indicated that CFMM may be a useful tool for the identification of essential proteins in the future.

【 授权许可】

Unknown