Gene ontology based functional analysis and graph theory for partitioning gene interaction networks - SREEJA ASHOK,; DR U.KRISHNAKUMAR, (International Journal of Pharma and Bio Sciences, 2017)
Exploring gene-disease associations improves the understanding of the underlying cause of the disease, which leads to further improvements in the diagnosis and treatment. Since genes that belong to the same topological, functional or disease module has an increased tendency of being involved in the same disease or phenotype, cluster analysis is the efficient approach to identify functionally similar genes. The aim of this work is to identify biologically relevant gene clusters using graph theory, which is an essential and influential scientific tool for modeling and exploring interconnected groups. The current work exhibits a computationally efficient algorithm for improving the performance of community detection in graphs using edge pruning techniques. The algorithm does not demand a priori judgment on the size of communities; it helps in automatic detection of gene communities with better performance. The optimized and streamlined approach is applied on cancer dataset and is compared and validated with standard clustering solutions using different validation measures.