The Applications of Clustering Methods in Predicting Protein Functions

被引：0

作者：

Chen, Weiyang ^{[1
]}

Li, Weiwei ^{[1
]}

Huang, Guohua ^{[2
]}

Flavel, Matthew ^{[3
]}

机构：

[1] Qilu Univ Technol, Shandong Acad Sci, Coll Informat, Jinan, Shandong, Peoples R China

[2] Shaoyang Univ, Coll Informat Engn, Shaoyang 422000, Hunan, Peoples R China

[3] La Trobe Univ, Sch Life Sci, Bundoora, Vic 3083, Australia

来源：

CURRENT PROTEOMICS | 2019年 / 16卷 / 05期

基金：

中国国家自然科学基金;

关键词：

Clustering; protein function prediction; protein-protein interaction; protein complexes; computational methods; topology; K-MEANS; SIMILARITY NETWORKS; INTERACTION GRAPHS; ALGORITHM; MODEL; COMPLEXES; IDENTIFICATION; MODULES;

D O I：

10.2174/1570164616666181212114612

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

Background: The understanding of protein function is essential to the study of biological processes. However, the prediction of protein function has been a difficult task for bioinformatics to overcome. This has resulted in many scholars focusing on the development of computational methods to address this problem. Objective: In this review, we introduce the recently developed computational methods of protein function prediction and assess the validity of these methods. We then introduce the applications of clustering methods in predicting protein functions.

引用

页码：354 / 358

页数：5

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