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

Hei-Chia Wang, Yi Shiun Lee

研究成果: Conference article同行評審

5 引文 斯高帕斯(Scopus)

摘要

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.

原文English
頁(從 - 到)309-317
頁數9
期刊Lecture Notes in Computer Science
3482
發行號III
出版狀態Published - 2005 九月 26
事件International Conference on Computational Science and Its Applications - ICCSA 2005 - , Singapore
持續時間: 2005 五月 92005 五月 12

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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