Advancing from protein interactomes and gene co-expression networks towards multi-omics-based composite networks: approaches for predicting and extracting biological knowledge. (17th July 2020)

• Record Type: Journal Article
• Authors: Randhawa, Vinay; Pathania, Shivalika
• Is Part Of: Briefings in functional genomics. Volume 19:Number 5/6(2020)
• Page Start: 364
• Page End: 376
• Publication Date: 2020-07-17
• Subjects: gene co-expression network -- interactome modelling -- protein interactome -- machine learning -- multi-omics data integration; Genomics -- Methodology -- Periodicals; Genomics -- Technological innovations -- Periodicals; 572.86072
• DOI: 10.1093/bfgp/elaa015
• Languages: English
• ISSNs: 2041-2649

Abstract

Abstract: Prediction of biological interaction networks from single-omics data has been extensively implemented to understand various aspects of biological systems. However, more recently, there is a growing interest in integrating multi-omics datasets for the prediction of interactomes that provide a global view of biological systems with higher descriptive capability, as compared to single omics. In this review, we have discussed various computational approaches implemented to infer and analyze two of the most important and well studied interactomes: protein–protein interaction networks and gene co-expression networks. We have explicitly focused on recent methods and pipelines implemented to infer and extract biologically important information from these interactomes, starting from utilizing single-omics data and then progressing towards multi-omics data. Accordingly, recent examples and case studies are also briefly discussed. Overall, this review will provide a proper understanding of the latest developments in protein and gene network modelling and will also help in extracting practical knowledge from them.