In-silico inferences for expression data using IGAM： Applied to Fuzzy-Clustering ＆ Regulatory Network Modeling

연판 지식을 이용한 유전자 발현 데이터 분석： 퍼지 플러스링과 조절 네트웍 모델링에의 응용

Genome-scale expression data provides us with valuable insights about organisms, but the biological validation of in-silico 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 5. Cerevisiae. The result was found to be more effective for extracting biological meanings from in-silico inferences for gene expression data.