Central hubs prediction for bio networks by directed hypergraph - GA with validation to COVID-19 PPI.

• Neoteric representation of biological networks by weighted directed hypergraph. • Identification of influential nodes using weak tie of directed hypergraph. • Hybridisation of degree centralities with genetic algorithm for optimizing the weights. • Reduced complexity by means of much lesser hyperedges compared to edges and compared results with graph centrality measures. • Influential COVID-19 proteins are identified from protein interactions which could be used for drug design. Network structures have attracted much interest and have been rigorously studied in the past two decades. Researchers used many mathematical tools to represent these networks, and in recent days, hypergraphs play a vital role in this analysis. This paper presents an efficient technique to find the influential nodes using centrality measure of weighted directed hypergraph. Genetic Algorithm is exploited for tuning the weights of the node in the weighted directed hypergraph through which the characterization of the strength of the nodes, such as strong and weak ties by statistical measurements (mean, standard deviation, and quartiles) is identified effectively. Also, the proposed work is applied to various biological networks for identification of influential nodes and results shows the prominence the work over the existing measures. Furthermore, the technique has been applied to COVID-19 viral protein interactions. The proposed algorithm identified some critical human proteins that belong to the enzymes TMPRSS2, ACE2, and AT-II, which have a considerable role in hosting COVID-19 viral proteins and causes for various types of diseases. Hence these proteins can be targeted in drug design for an effective therapeutic against COVID-19. [ABSTRACT FROM AUTHOR]