ABSTRACT

Protein-protein interaction networks were analysed to identify proteins with high between-ness values and their occurrence in the network. Subgraphs of human protein interactome to identify important groups of proteins based on various centralities.  A disease-disease network was created with edge weight based on shared proteins. Degree distribution of the network was compared with standard network models.

Description
Multiple databases provide free access to protein-protein interaction data. Graph theory provides powerful tools to analyse such data. The analysis has multiple possible applications like, prediction of interaction of a new protein with the proteins in the database (how would a new disease protein effect human biochemistry?) ,  identification of roles of special proteins in processes (which proteins to target to inhibit or enhance certain processes) and identification of functional groups of proteins (which proteins play a role in metabolic processes ?). We have studies network representation of protein data to identify proteins with special roles and their relation to the structure of the network. 

Outcomes: Protein-protein interaction networks were analysed to identify proteins with high between-ness values and their occurrence in the network. Subgraphs of human protein interactome to identify important groups of proteins based on various centralities.  A disease-disease network was created with edge weight based on shared proteins. Degree distribution of the network was compared with standard network models.

Other Members: Manish Datt, Seema Aswani, Priyanka Nimawat 

Keywords: Data Science: Cloud Computing, Data Analytics and Machine Learning