Finding Influential Genes Using Gene Expression Data and Boolean Models of Metabolic Networks

Takeyuki Tamura, Tatsuya Akutsu, Chun-Yu Lin, Jinn-Moon Yang

研究成果: Conference contribution同行評審

摘要

Selection of influential genes using gene expression data from normal and disease samples is an important topic in bioinformatics. In this paper, we propose a novel computational method for the problem, which combines gene expression patterns from normal and disease samples with a mathematical model of metabolic networks. This method seeks a set of k genes knockout of which drives the state of the metabolic network towards that in the disease samples. We adopt a Boolean model of metabolic networks and formulate the problem as a maximization problem under an integer linear programming framework. We applied the proposed method to selection of influential genes using gene expression data from normal samples and disease (head and neck cancer) samples. The result suggests that the proposed method can select more biologically relevant genes than an existing P-value based ranking method can.

原文English
主出版物標題Proceedings - 2016 IEEE 16th International Conference on Bioinformatics and Bioengineering, BIBE 2016
發行者Institute of Electrical and Electronics Engineers Inc.
頁面57-63
頁數7
ISBN(電子)9781509038336
DOIs
出版狀態Published - 16 12月 2016
事件16th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2016 - Taichung, Taiwan
持續時間: 31 10月 20162 11月 2016

出版系列

名字Proceedings - 2016 IEEE 16th International Conference on Bioinformatics and Bioengineering, BIBE 2016

Conference

Conference16th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2016
國家/地區Taiwan
城市Taichung
期間31/10/162/11/16

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