Frequent Pattern Discovery in Multiple Biological Networks: Patterns and Algorithms

被引：4
|
作者：
Li W. [1 ]
Hu H. [2 ]
Huang Y. [1 ]
Li H. [3 ]
Mehan M.R. [1 ]
Nunez-Iglesias J. [1 ]
Xu M. [1 ]
Yan X. [4 ]
Zhou X.J. [1 ]
机构：
[1] Program in Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles
[2] School of Electrical Engineering and Computer Science, University of Central Florida, Orlando
[3] Motorola Labs, 2900 S Diablo Way, Tempe
[4] Computer Science Department, University of California at Santa Barbara, Santa Barbara
来源：
Statistics in Biosciences | 2012年 / 4卷 / 1期
基金：
美国国家科学基金会; 美国国家卫生研究院;
关键词：
Coherent dense subgraph; Differential subgraph; Frequent dense vertex-set; Frequent pattern; Generic frequent subgraph; Integrative network analysis; Recurrent heavy subgraph; Tensor representation of multiple networks;
D O I：
10.1007/s12561-011-9047-0
中图分类号：
学科分类号：
摘要：
The rapid accumulation of biological network data is creating an urgent need for computational methods capable of integrative network analysis. This paper discusses a suite of algorithms that we have developed to discover biologically significant patterns that appear frequently in multiple biological networks: coherent dense subgraphs, frequent dense vertex-sets, generic frequent subgraphs, differential subgraphs, and recurrent heavy subgraphs. We demonstrate these methods on gene co-expression networks, using the identified patterns to systematically annotate gene functions, map genome to phenome, and perform high-order cooperativity analysis. © 2011 The Author(s).
引用
收藏
页码：157 / 176
页数：19
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