Discovering gene clusters via integrated analysis on time-series and group-comparative microarray datasets

S. Tseng*, Lien Chin Chen, Yao Dung Hsieh

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper, we propose a novel gene clustering method named TGmix through integrated analysis on two types of datasets, namely the time-series and two-group microarray datasets. The goal of the proposed method is to discover genes as biomarkers that have similar expression profiles in time-series conditions and are also significantly differentially expressed in two-group conditions. We applied the proposed method to microarray datasets for rat's wound healing experiment, and the genes discovered in the same cluster conform to the analysis goal with related biological functions.

Original languageEnglish
Title of host publicationProceedings - 19th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2006
Pages177-182
Number of pages6
DOIs
StatePublished - 22 Dec 2006
Event19th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2006 - Salt Lake City, UT, United States
Duration: 22 Jun 200623 Jun 2006

Publication series

NameProceedings - IEEE Symposium on Computer-Based Medical Systems
Volume2006
ISSN (Print)1063-7125

Conference

Conference19th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2006
CountryUnited States
CitySalt Lake City, UT
Period22/06/0623/06/06

Fingerprint Dive into the research topics of 'Discovering gene clusters via integrated analysis on time-series and group-comparative microarray datasets'. Together they form a unique fingerprint.

Cite this