Executive Summary : | The human genome project has led researchers to focus on molecular biology research, which involves analyzing large amounts of data from high-end machines, including DNA/protein sequences and transcripts expression, for normal and cancerous cells. These data are often interpreted in terms of biological networks, such as protein-protein interaction, isoform-isoform, genetic interaction, metabolic, brain, and drug-target networks. The main objective is to develop algorithms for detecting modules in biological networks and interpreting them as functional units of disease propagation, particularly cancer. The proposal aims to identify key regulatory elements within these modules that are influential and trigger cancer, and validate these elements through experimental validation with collaboration with experimental laboratories. Existing algorithms for module detection in social networks are limited, with most being for protein-protein interaction and brain networks and detecting non-overlapping modules. The research group has already attempted module detection algorithms for general networks, but it is essential to consider biological networks. Other problems include detecting modularity based on the integration of all omics and pathological data and strength measures for modularity. The development of algorithms will be achieved using C++, Python, MATLAB, and R programming, and validated using modularity measures, stability (NMI), complexity, and ROC. Finally, the goal is to identify key regulatory elements within the cells, which may be potent biomarkers for early cancer diagnosis and prognosis. |