I am a Reseach Scholar (M.S.) in Department of Computer Science and Engineering. My work majorly focusses on machine learning applications to biological data. In the field of biology, complex network analysis has a special importance as exhaustive characterizing of genes or proteins through biological experiments is intractable. So leveraging the power of computation and available knowledge several hypotheses can be made to guide the experiments. The basic assumption follows “guilt by association” principle where genes or proteins that are colocalized or have similar topological roles are functionally correlated allowing us to infer properties of unknown genes. But inferencing from the biological network using traditional clustering algorithms for extracting biologically meaningful functional modules is tough due to the presence of noisy false positive interactions. Moreover, there are heterogeneous sources of information available so the challenge lies in developing integrative methods which can take advantage of the topology of the multiple heterogeneous networks available. These help in providing stronger confidence on the predictions made for unknown genes. We have proposed several methods and heuristics which gives significant improvement in performance.