Employing Social Network Analysis for Disease Biomarker Detection

Abstract

Detection of disease biomarkers in general and cancer biomarkers in particular is an important task which has received considerable attention in the area of in silico genomic experiments. We describe a new approach for detecting cancer biomarkers based on genomic microarray data; it is characterised by employing Social Network Analysis (SNA) techniques. Through social interaction perspective, we can have genes as actors in a social network, where similarities between genes can be described as connections between these actors. The correct determination of biomarkers out of huge genomic data dramatically decreases the number of features. It is also possible to achieve the same or better classification performance compared to using the whole data. The minimum number of biomarkers can be researched further biologically to reduce the numerous time-consuming in vitro experiments. Results of the conducted experiments with selected biomarkers are promising and efficient.