@inproceedings{c13b60d4d28b426b9c28188330a7843c,
title = "Finding Influential Genes Using Gene Expression Data and Boolean Models of Metabolic Networks",
abstract = "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.",
keywords = "Driver genes, Gene expression, Marker genes, Metabolic networks",
author = "Takeyuki Tamura and Tatsuya Akutsu and Chun-Yu Lin and Jinn-Moon Yang",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 16th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2016 ; Conference date: 31-10-2016 Through 02-11-2016",
year = "2016",
month = dec,
day = "16",
doi = "10.1109/BIBE.2016.25",
language = "English",
series = "Proceedings - 2016 IEEE 16th International Conference on Bioinformatics and Bioengineering, BIBE 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "57--63",
booktitle = "Proceedings - 2016 IEEE 16th International Conference on Bioinformatics and Bioengineering, BIBE 2016",
address = "美國",
}
