NetCoffee2: A Novel Global Alignment Algorithm for Multiple PPI Networks Based on Graph Feature Vectors

被引：4

作者：

Hu, Jialu ^{[1
]}

He, Junhao ^{[1
]}

Gao, Yiqun ^{[1
]}

Zheng, Yan ^{[1
]}

Shang, Xuequn ^{[1
]}

机构：

[1] Northwestern Polytech Univ, Sch Comp Sci, 1 Dongxiang Rd, Xian 710129, Shaanxi, Peoples R China

来源：

INTELLIGENT COMPUTING THEORIES AND APPLICATION, PT II | 2018年 / 10955卷

基金：

中国国家自然科学基金; 中国博士后科学基金;

关键词：

PPI network alignment; Simulated annealing; Functionally conserved proteins; MAXIMIZING ACCURACY;

D O I：

10.1007/978-3-319-95933-7_30

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Network alignment provides a fast and effective framework to automatically identify functionally conserved proteins in a systematic way. However, due to the fast growing biological data, there is an increasing demand for more accurate and efficient tools to deal with multiple PPI networks. Here, we present a novel global alignment algorithm NetCoffee2 to discover functionally conserved proteins. To test the algorithm performance, NetCoffee2 and several existing algorithms were applied on eight real biological datasets. Results show that NetCoffee2 is superior to IsoRankN, NetCoffee and multiMAGNA++ in terms of both coverage and consistency. The binary and source code are freely available at https://github.com/screamer/NetCoffee2.

引用

页码：241 / 246

页数：6

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