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Author(s): Honda, H. | Kobayashi, T. | Takahashi, H. | Tomida, S. |
Year: 2003
Citation: JOURNAL OF BIOSCIENCE AND BIOENGINEERING Volume: 96 Issue: 2 Pages: 154-160
Abstract: Inferring genetic networks from gene expression data is the most challenging work in the postgenomic 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 H2O2. 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.
Topics:
Machine Learning,
Applications:
Chemical Analysis,
Models:
ART 2 / Fuzzy ART,
