Inference of common genetic network using fuzzy adaptive resonance theory associated matrix method

Hiro Takahashi, Shuta Tomida, Takeshi Kobayashi, Hiroyuki Honda

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


Inferring genetic networks from gene expression data is the most challenging work in the post-genomic era. However, most studies tend to show their genetic network inference ability by using artificial data. Here, we developed the fuzzy adaptive resonance theory associated matrix (F-ART matrix) method to infer genetic networks and applied it to experimental time series data, which are gene expression profiles of Saccharomyces cerevisiae responding under oxidative stresses such as diamide, heat shock and H2O 2. We preprocessed them using the fuzzy adaptive resonance theory and successfully identified genetic interactions by drawing a 2-dimensional matrix. The identified interactions between diamide and heat shock stress were confirmed to be the common interactions for two stresses, compared with the KEGG metabolic map, BRITE protein interaction map, and gene interaction data of other papers. In the predicted common genetic network, the hit ratio was 60% for the KEGG map. Several gene interactions were also drawn, which have been reported to be important in oxidative stress. This result suggests that F-ART matrix has the potential to function as a new method to extract the common genetic networks of two different stresses using experimental time series microarray data.

Original languageEnglish
Pages (from-to)154-160
Number of pages7
JournalJournal of Bioscience and Bioengineering
Issue number2
Publication statusPublished - 2003
Externally publishedYes


  • Clustering
  • Fuzzy adaptive resonance theory
  • Gene expression profile
  • Genetic network
  • Oxidative stress

ASJC Scopus subject areas

  • Biotechnology
  • Bioengineering
  • Applied Microbiology and Biotechnology


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