Abstract
It is fundamental and essential to elucidate how cancerrelated genes interact with each other. In this study, we build two undirected graphs: one is a graph consisting of edges only observed in tumor samples, and the other is a graph consisting of edges only observed in normal samples. We apply a genetic algorithm for searching sub-networks of these genetic networks. Those gene sub-networks identify new cancer-related genes that might be related with previously known cancer-related genes, and also show a higher accuracy in classifying tumor and normal samples than the current methods.
| Original language | English |
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| Title of host publication | 2010 ACM International Conference on Bioinformatics and Computational Biology, ACM-BCB 2010 |
| Pages | 493-495 |
| Number of pages | 3 |
| DOIs | |
| Publication status | Published - 2010 |
| Event | 2010 ACM International Conference on Bioinformatics and Computational Biology, ACM-BCB 2010 - Niagara Falls, NY, United States Duration: 2010 Aug 2 → 2010 Aug 4 |
Publication series
| Name | 2010 ACM International Conference on Bioinformatics and Computational Biology, ACM-BCB 2010 |
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Other
| Other | 2010 ACM International Conference on Bioinformatics and Computational Biology, ACM-BCB 2010 |
|---|---|
| Country | United States |
| City | Niagara Falls, NY |
| Period | 10/8/2 → 10/8/4 |
All Science Journal Classification (ASJC) codes
- Biomedical Engineering
- Health Information Management
UN SDGs
This output contributes to the following Sustainable Development Goal(s)
