Integration of Network Biology and Machine Learning for Identification and Prioritisation of Breast Cancer Targets
Banudevapuram Sai Spandana · International Journal for Research in Applied Science and Engineering Technology · 2025
Breast cancer is a complex disease involving multiple genes and proteins. Identifying key proteins and their interactions is crucial for understanding the disease mechanisms and developing targeted therapies. This study employs a network-based approach to analyze protein-protein interaction (PPI) data related to breast cancer, utilizing the PageRank algorithm and random forest classifier. Breast cancer-related PPI 984data was obtained from the STRING database and processed using Python libraries such as pandas and networkx. Topological analysis was performed to identify central proteins based on degree, betweenness, closeness, and eigenvector centrality measures. The PageRank algorithm was applied to rank proteins by their importance in the network. A random forest classifier was trained using the PageRank scores and known cancer relevance labels to predict the cancer relevance of proteins. Additionally, molecular docking simulations were conducted using AutoDock Vina to evaluate the binding affinities of PARP inhibitors (Niraparib, Olaparib, Veliparib, and Rucaparib) to the PARP1 protein. The docking results were rescored using the DeltaVina RF scoring function, which combines the Vina scoring function with a random forest approach. The study identified key proteins involved in breast cancer, with the top-ranked proteins being ENSP00000418960, ENSP00000260947, and \ENSP00000278616. The random forest classifier achieved perfect accuracy in predicting cancer relevance based on PageRank scores. Molecular docking and rescoring revealed Niraparib and Veliparib as the most promising PARP inhibitors. This study demonstrates the utility of combining network analysis, machine learning, and molecular docking techniques to identify potential drug targets and evaluate drug candidates for breast cancer treatment.