A Network Partition Algorithm for Mining Gene Functional Modules of Colon Cancer from DNA Microarray Data - Details

Author：

Ruan, X.-G. (Ruan, X.-G..) | Wang, J.-L. (Wang, J.-L..) | Li, J.-G. (Li, J.-G..)

Indexed by：

Scopus

Abstract：

Computational　analysis　is　essential　for　transforming　the　masses　of　microarray　data　into　a　mechanistic　understanding　of　cancer.　Here　we　present　a　method　for　finding　gene　functional　modules　of　cancer　from　microarray　data　and　have　applied　it　to　colon　cancer.　First,　a　colon　cancer　gene　network　and　a　normal　colon　tissue　gene　network　were　constructed　using　correlations　between　the　genes.　Then　the　modules　that　tended　to　have　a　homogeneous　functional　composition　were　identified　by　splitting　up　the　network.　Analysis　of　both　networks　revealed　that　they　are　scale-free.　Comparison　of　the　gene　functional　modules　for　colon　cancer　and　normal　tissues　showed　that　the　modules＇　functions　changed　with　their　structures.　©　2006　Beijing　Genomics　Institute.

Keyword：

colon cancer; DNA microarray data; gene functional module; GN algorithm

Author Community：

[ 1 ] [Ruan, X.-G.]Institute of Artificial Intelligence and Robotics, School of Electronic Information and Control Engineering, Beijing University of Technology, Beijing, 100022, China
[ 2 ] [Wang, J.-L.]Institute of Artificial Intelligence and Robotics, School of Electronic Information and Control Engineering, Beijing University of Technology, Beijing, 100022, China
[ 3 ] [Li, J.-G.]Institute of Artificial Intelligence and Robotics, School of Electronic Information and Control Engineering, Beijing University of Technology, Beijing, 100022, China

Reprint Author's Address：

[Wang, J.-L.]Institute of Artificial Intelligence and Robotics, School of Electronic Information and Control Engineering, Beijing University of Technology, Beijing, 100022, China

Email：

wjinlian1999@emails.bjut.edu.cn

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Source ：

Genomics, Proteomics and Bioinformatics

ISSN： 1672-0229

Year： 2006

Issue： 4

Volume： 4

Page： 245-252

Cited Count：

WoS CC Cited Count： 0

SCOPUS Cited Count： 3

ESI Highly Cited Papers on the List： 0 Unfold All

WanFang Cited Count：

Chinese Cited Count：

30 Days PV： 3

Affiliated Colleges：

信息科学技术学院本学院/部未明确归属的数据

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