Gene network prediction from microarray data by association rule and dynamic Bayesian network

Hei-Chia Wang, Yi Shiun Lee

Research output: Contribution to journalConference article

5 Citations (Scopus)

Abstract

Using microarray technology to predict gene function has become important in research. However, microarray data are complicated and require a powerful systematic method to handle these data. Many scholars use clustering algorithms to analyze microarray data, but these algorithms can find only the same expression mode, not the transcriptional relation between genes. Moreover, most traditional approaches involve all-against-all comparisons that are time consuming. To reduce the comparison time and find more relations, a proposed method is to use an a priori algorithm to filter possible related genes first, which can reduce number of candidate genes, and then apply a dynamic Bayesian network to find the gene's interaction. Unlike the previous techniques, this method not only reduces the comparison complexity but also reveals more mutual interaction among genes.

Original languageEnglish
Pages (from-to)309-317
Number of pages9
JournalLecture Notes in Computer Science
Volume3482
Issue numberIII
Publication statusPublished - 2005 Sep 26
EventInternational Conference on Computational Science and Its Applications - ICCSA 2005 - , Singapore
Duration: 2005 May 92005 May 12

Fingerprint

Dynamic Bayesian Networks
Gene Networks
Association rules
Association Rules
Bayesian networks
Microarrays
Microarray Data
Genes
Gene
Prediction
Interaction
Clustering algorithms
Microarray
Clustering Algorithm
Filter
Predict

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

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Gene network prediction from microarray data by association rule and dynamic Bayesian network. / Wang, Hei-Chia; Lee, Yi Shiun.

In: Lecture Notes in Computer Science, Vol. 3482, No. III, 26.09.2005, p. 309-317.

Research output: Contribution to journalConference article

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AU - Lee, Yi Shiun

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