Computational prediction and analysis of protein-protein interaction networks

TL;DR

This paper discusses computational methods for predicting, aligning, and analyzing protein-protein interaction networks, highlighting recent advances including deep learning approaches for network reconstruction.

Contribution

It introduces new methods for protein-protein interaction network alignment and explores deep learning techniques for network reconstruction.

Findings

Comparison of network alignment methods

Insights into functional modules and pathways

Application of deep learning for network reconstruction

Abstract

Biological networks provide insight into the complex organization of biological processes in a cell at the system level. They are an effective tool for understanding the comprehensive map of functional interactions, finding the functional modules and pathways. Reconstruction and comparative analysis of these networks provide useful information to identify functional modules, prioritization of disease causing genes and also identification of drug targets. The talk will consist of two parts. I will discuss several methods for protein-protein interaction network alignment and investigate their preferences to other existing methods. Further, I briefly talk about reconstruction of protein-protein interaction networks by using deep learning.