Inferring the transcriptional modules using penalized matrix decomposition

Chun Hou Zheng, Lei Zhang, Vincent To Yee Ng, Chi Keung Simon Shiu, Shu Lin Wang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

3 Citations (Scopus)

Abstract

This paper proposes to use the penalized matrix decomposition (PMD) to discover the transcriptional modules from microarray data. With the sparsity constraint on the decomposition factors, metagenes can be extracted from the gene expression data and they can well capture the intrinsic patterns of genes with the similar functions. Meanwhile, the PMD factors of each gene are good indicators of the cluster it belongs to. Compared with traditional methods, our method can cluster genes of the similar functions but without similar expression profiles. It can also assign a gene into different modules.
Original languageEnglish
Title of host publicationAdvanced Intelligent Computing Theories and Applications
Subtitle of host publicationWith Aspects of Artificial Intelligence - 6th International Conference on Intelligent Computing, ICIC 2010, Proceedings
Pages35-41
Number of pages7
DOIs
Publication statusPublished - 6 Sep 2010
Event6th International Conference on Intelligent Computing, ICIC 2010 - Changsha, China
Duration: 18 Aug 201021 Aug 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6216 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference6th International Conference on Intelligent Computing, ICIC 2010
CountryChina
CityChangsha
Period18/08/1021/08/10

Keywords

  • Clustering
  • Gene expression data
  • Penalized matrix decomposition
  • Transcriptional module

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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