Graph-Theory Based Simplification Techniques for Efficient Biological Network Analysis

The recent years have witnessed the remarkable expansion of publicly available biological data in the related research fields. Many researches in these fields often require massive data to be analyzed by utilizing high-throughput sequencing technologies. However, it is very challenging to interpret the data efficiently due to it high complexity. This paper introduces two new graph algorithms which aim to improve the efficiency of the existing methods for biological network data interpretation. In particular, the algorithms focus on the problem of how to simplify gene regulatory networks so that many existing algorithms can efficiently discover important connected components of a biological system in their own context as many times as they need. The performance of the proposed algorithms is compared with each other with gene expression data of glioblastoma brain tumor cancer.