Literature DB >> 15374868

Identifying time-lagged gene clusters using gene expression data.

Liping Ji1, Kian-Lee Tan.   

Abstract

MOTIVATION: Analysis of gene expression data can provide insights into the time-lagged co-regulation of genes/gene clusters. However, existing methods such as the Event Method and the Edge Detection Method are inefficient as they compare only two genes at a time. More importantly, they neglect some important information due to their scoring criterian. In this paper, we propose an efficient algorithm to identify time-lagged co-regulated gene clusters. The algorithm facilitates localized comparison and processes several genes simultaneously to generate detailed and complete time-lagged information for genes/gene clusters.
RESULTS: We experimented with the time-series Yeast gene dataset and compared our algorithm with the Event Method. Our results show that our algorithm is not only efficient, but also delivers more reliable and detailed information on time-lagged co-regulation between genes/gene clusters. AVAILABILITY: The software is available upon request. CONTACT: jiliping@comp.nus.edu.sg SUPPLEMENTARY INFORMATION: Supplementary tables and figures for this paper can be found at http://www.comp.nus.edu.sg/~jiliping/p2.htm.

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Year:  2004        PMID: 15374868     DOI: 10.1093/bioinformatics/bti026

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  20 in total

1.  Enrichment constrained time-dependent clustering analysis for finding meaningful temporal transcription modules.

Authors:  Jia Meng; Shou-Jiang Gao; Yufei Huang
Journal:  Bioinformatics       Date:  2009-04-07       Impact factor: 6.937

2.  Efficient statistical significance approximation for local similarity analysis of high-throughput time series data.

Authors:  Li C Xia; Dongmei Ai; Jacob Cram; Jed A Fuhrman; Fengzhu Sun
Journal:  Bioinformatics       Date:  2012-11-23       Impact factor: 6.937

3.  Dynamics of time-lagged gene-to-metabolite networks of Escherichia coli elucidated by integrative omics approach.

Authors:  Hiroki Takahashi; Ryoko Morioka; Ryosuke Ito; Taku Oshima; Md Altaf-Ul-Amin; Naotake Ogasawara; Shigehiko Kanaya
Journal:  OMICS       Date:  2010-09-23

4.  Reconstruct gene regulatory network using slice pattern model.

Authors:  Yadong Wang; Guohua Wang; Bo Yang; Haijun Tao; Jack Y Yang; Youping Deng; Yunlong Liu
Journal:  BMC Genomics       Date:  2009-07-07       Impact factor: 3.969

5.  A temporal precedence based clustering method for gene expression microarray data.

Authors:  Ritesh Krishna; Chang-Tsun Li; Vicky Buchanan-Wollaston
Journal:  BMC Bioinformatics       Date:  2010-01-30       Impact factor: 3.169

6.  A polynomial time biclustering algorithm for finding approximate expression patterns in gene expression time series.

Authors:  Sara C Madeira; Arlindo L Oliveira
Journal:  Algorithms Mol Biol       Date:  2009-06-04       Impact factor: 1.405

7.  Identification of temporal association rules from time-series microarray data sets.

Authors:  Hojung Nam; KiYoung Lee; Doheon Lee
Journal:  BMC Bioinformatics       Date:  2009-03-19       Impact factor: 3.169

8.  BiGGEsTS: integrated environment for biclustering analysis of time series gene expression data.

Authors:  Joana P Gonçalves; Sara C Madeira; Arlindo L Oliveira
Journal:  BMC Res Notes       Date:  2009-07-07

9.  Global screening of potential Candida albicans biofilm-related transcription factors via network comparison.

Authors:  Yu-Chao Wang; Chung-Yu Lan; Wen-Ping Hsieh; Luis A Murillo; Nina Agabian; Bor-Sen Chen
Journal:  BMC Bioinformatics       Date:  2010-01-26       Impact factor: 3.169

10.  Maximization of negative correlations in time-course gene expression data for enhancing understanding of molecular pathways.

Authors:  Tao Zeng; Jinyan Li
Journal:  Nucleic Acids Res       Date:  2009-10-23       Impact factor: 16.971

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