A preprocessing method for inferring genetic interaction from gene expression data using Boolean algorithm

Kazumi Hakamada, Taizo Hanai, Hiroyuki Honda, Takeshi Kobayashi

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

Unknown genetic regulation mechanisms are expected to be discovered by information technology using large amount of biological data especially for gene expression data. In this study, we propose a novel inferring method for genetic interactions that combines our original preprocessing method and the Boolean algorithm. First, the performance of our method was evaluated using artificial data. The results showed that our method was able to infer genetic interactions with high specificity (specificity=0.629). Then, using our method, the genetic interaction was inferred from the experimental time course data collected using microarray on 69 genes of cell cycle for Saccharomyces cerevisiae. Our method estimated about 80% of all genetic interactions in Kyoto Encyclopedia Genes and Genomes (KEGG) for these genes. Furthermore, our method was able to infer several other genetic interactions that are not included in KEGG but whose existence is supported by other biological reports.

Original languageEnglish
Pages (from-to)457-463
Number of pages7
JournalJournal of Bioscience and Bioengineering
Volume98
Issue number6
DOIs
Publication statusPublished - Dec 2004

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All Science Journal Classification (ASJC) codes

  • Biotechnology
  • Bioengineering
  • Applied Microbiology and Biotechnology

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