Efficient inferring method of genetic interactions based on time-series of gene expression profile

Masahiko Nakatsui, Takanori Ueda, Yukihiro Maki, Isao Ono, Masahiro Okamoto

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Recent advances in technologies such as DNA microarrays have provided a mass of gene expression data on the genomic scale. One of the most important projects in post-genome-era is the systemic identification of gene expression networks. However, inferring internal gene expression structure from experimentally observed time-series data is 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 among various network candidates. Common core binomial genetic interactions are reliable interactions and are important in understanding the dynamic behavior of gene expression network. Here, we discuss an efficient method for inferring genetic interactions that combines a Step-by-step strategy [1] with an analytical method for extracting common core binomial genetic interactions.

Original languageEnglish
Title of host publicationProceedings of the 13th International Symposium on Artificial Life and Robotics, AROB 13th'08
Pages71-76
Number of pages6
Publication statusPublished - 2008
Event13th International Symposium on Artificial Life and Robotics, AROB 13th'08 - Oita, Japan
Duration: Jan 31 2008Feb 2 2008

Publication series

NameProceedings of the 13th International Symposium on Artificial Life and Robotics, AROB 13th'08

Other

Other13th International Symposium on Artificial Life and Robotics, AROB 13th'08
Country/TerritoryJapan
CityOita
Period1/31/082/2/08

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction

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