Name
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. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/BIBE.2016.25 | 2016 IEEE 16th International Conference on Bioinformatics and Bioengineering (BIBE) |
Keywords | Field | DocType |
gene expression,metabolic networks,marker genes,driver genes | Gene,Ranking,Computer science,Metabolic network,Boolean model,Gene expression,Integer programming,Artificial intelligence,Bioinformatics,Maximization,Machine learning | Conference |
ISSN | ISBN | Citations |
2471-7819 | 978-1-5090-3835-0 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Takeyuki Tamura | 1 | 210 | 23.66 |
| Tatsuya Akutsu | 2 | 2169 | 216.05 |
| Chun-Yu Lin | 3 | 379 | 74.29 |
| Jinn-moon Yang | 4 | 364 | 35.89 |