Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6487094 | Computational Biology and Chemistry | 2015 | 9 Pages |
Abstract
In this study we discuss critical issues and challenges in existing computational approaches for tumor stratification. We show that the problem can be formulated as finding densely connected sub-graphs (bi-cliques) in a bipartite graph representation of genomic data. We propose a novel algorithm that takes advantage of prior biology knowledge through a gene-gene interaction network to find such sub-graphs, which helps simultaneously identify both tumor subtypes and their corresponding genetic markers. Our experimental results show that our proposed method outperforms current state-of-the-art methods for tumor stratification.
Keywords
Related Topics
Physical Sciences and Engineering
Chemical Engineering
Bioengineering
Authors
Amin Ahmadi Adl, Xiaoning Qian,