Method for inferring and extracting reliable genetic interactions from time-series profile of gene expression
Masahiko Nakatsui1, Takanori Ueda, Yukihiro Maki
1Laboratory for Bioinformatics, Graduate School of Systems Life Sciences, Kyushu University, Fukuoka 812-8581, Japan.
Abstract:
Recent advances in technologies such as DNA microarrays have provided an abundance of gene expression data on the genomic scale. One of the most important projects in the post-genome-era is the systemic identification of gene expression networks. However, inferring internal gene expression structure from experimentally observed time-series data are an inverse problem. We have therefore developed a system for inferring network candidates based on experimental observations. Moreover, we have proposed an analytical method for extracting common core binomial genetic interactions from various network candidates. Common core binomial genetic interactions are reliable interactions with a higher possibility of existence, and are important for understanding the dynamic behavior of gene expression networks. Here, we discuss an efficient method for inferring genetic interactions that combines a Step-by-step strategy (Y. Maki, Y. Takahashi, Y. Arikawa, S. Watanabe, K. Aoshima, Y. Eguchi, T. Ueda, S. Aburatani, S. Kuhara, M. Okamoto, An integrated comprehensive workbench for inferring genetic networks: Voyagene, Journal of Bioinformatics and Computational Biology 2(3) (2004) 533.) with an analysis method for extracting common core binomial genetic interactions.
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