02794nas a2200265   4500008004100000022001400041245009800055210006900153260000800222300000700230490000700237520190500244653001502149653002302164653003202187653002102219653003502240653002102275653002002296100002102316700001902337700002302356700002102379856012802400       2022                        eng d  a0250-599100aEmerging landscape of molecular interaction networks: opportunities, challenges and prospects0 aEmerging landscape of molecular interaction networks opportuniti  cAPR  a240 v473 a<p>
	Network biology finds application in interpreting molecular interaction networks and providing insightful inferences using graph theoretical analysis of biological systems. The integration of computational bio-modelling approaches with different hybrid network-based techniques provides additional information about the behaviour of complex systems. With increasing advances in high-throughput technologies in biological research, attempts have been made to incorporate this information into network structures, which has led to a continuous update of network biology approaches over time. The newly minted centrality measures accommodate the details of omics data and regulatory network structure information. The unification of graph network properties with classical mathematical and computational modelling approaches and technologically advanced approaches like machine-learning- and artificial intelligence-based algorithms leverages the potential application of these techniques. These computational advances prove beneficial and serve various applications such as essential gene prediction, identification of drug-disease interaction and gene prioritization. Hence, in this review, we have provided a comprehensive overview of the emerging landscape of molecular interaction networks using graph theoretical approaches. With the aim to provide information on the wide range of applications of network biology approaches in understanding the interaction and regulation of genes, proteins, enzymes and metabolites at different molecular levels, we have reviewed the methods that utilize network topological properties, emerging hybrid network-based approaches and applications that integrate machine learning techniques to analyse molecular interaction networks. Further, we have discussed the applications of these approaches in biomedical research with a note on future prospects.</p>
10aCentrality10adisease mechanisms10ahybrid network-based models10amachine learning10amolecular interaction networks10anetwork topology10asystems biology1 aPanditrao, Gauri1 aBhowmick, Rupa1 aMeena, Chandrakala1 aSarkar, Ram, Rup  uhttps://library.ncl.res.in/content/emerging-landscape-molecular-interaction-networks-opportunities-challenges-and-prospects