Ranking gene regulatory network models with microarray data and Bayesian Network

Hongqiang Li, Mi Zhou, Yan Cui

Research output: Contribution to journalConference article

2 Citations (Scopus)

Abstract

Researchers often have several different hypothesises on the possible structures of the gene regulatory network (GRN) underlying the biological model they study. It would be very helpful to be able to rank the hypothesises using existing data. Microarray technologies enable us to monitor the expression levels of tens of thousands of genes simultaneously. Given the expression levels of almost all of the well-substantiated genes in an organism under many experimental conditions, it is possible to evaluate the hypothetical gene regulatory networks with statistical methods. We present RankGRN, a web-based tool for ranking hypothetical gene regulatory networks. RankGRN scores the gene regulatory network models against microarray data using Bayesian Network methods. The score reflects how well a gene network model explains the microarray data. A posterior probability is calculated for each network based on the scores. The networks are then ranked by their posterior probabilities. RankGRN is available online at [http://GeneNet.org/bn]. RankGRN is a useful tool for evaluating the hypothetical gene network models' capability of explaining the observational gene expression data (i.e. the microarray data). Users can select the gene network model that best explains the microarray data.

Original languageEnglish (US)
Pages (from-to)109-118
Number of pages10
JournalLecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
Volume3327
StatePublished - Dec 1 2004
EventChinese Academy of Sciences Symposium on Data Mining and Knowledge Management, CASDMKM 2004 - Beijing, China
Duration: Jul 12 2004Jul 14 2004

Fingerprint

Gene Regulatory Network
Bayesian networks
Microarrays
Microarray Data
Bayesian Networks
Network Model
Gene Networks
Ranking
Genes
Posterior Probability
Gene
Biological Models
Gene Expression Data
Microarray
Statistical method
Web-based
Monitor
Evaluate
Gene expression
Statistical methods

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Ranking gene regulatory network models with microarray data and Bayesian Network. / Li, Hongqiang; Zhou, Mi; Cui, Yan.

In: Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), Vol. 3327, 01.12.2004, p. 109-118.

Research output: Contribution to journalConference article

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