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

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

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

1 被引用数 (Scopus)

抄録

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.

本文言語英語
ホスト出版物のタイトルProceedings of the 13th International Symposium on Artificial Life and Robotics, AROB 13th'08
ページ71-76
ページ数6
出版ステータス出版済み - 12 1 2008
イベント13th International Symposium on Artificial Life and Robotics, AROB 13th'08 - Oita, 日本
継続期間: 1 31 20082 2 2008

出版物シリーズ

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

その他

その他13th International Symposium on Artificial Life and Robotics, AROB 13th'08
Country日本
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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