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In-Silico inferences for expression data using IGAM

- Applied to Fuzzy-Clustering & Regulatory Network Modeling

Vol.14 No.1, 2004.4, 273-276 (4 pages)
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Abstract
Genome-scale expression data provides us with valuable insights about organisms, but the biological validation of in-silica analysis is difficult and often controversial. Here we present a new approach for integrating previously established knowledge with computational analysis. Based on the known biological evidences, IGAM (Integrated Gene Association Matrix) automatically estimates the relatedness between a pair of genes. We combined this association knowledge to the regulatory network modeling and fuzzy clustering in yeast S. Cerevisiae. The result was found to be more effective for extracting biological meanings from in-silica inferences for gene expression data.

TOC
ABSTRACT
Ⅰ. INTRODUCTION
Ⅱ. METHODS
Ⅲ. RESULTS
Ⅳ. CONCLUSIONS
Ⅴ. ACKNOWLEDGEMENTS
Ⅵ. REFERENCES
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