A comprehensive review of global alignment of multiple biological networks: background, applications and open issues

Abstract

Alignment of biological data aims to transfer the functional knowledge across the species by comparing the data of a well-studied one with that of a less-studied thus gaining insights into the cell’s functioning from biological, chemical and physical perspectives. With the advancements in information communication technologies, imaging methods, etc., there is a heap of biological data that is getting accumulated at several databases across the web. This data can be analyzed to attain significant knowledge and to further develop an application that caters to the present and futuristic demands for personalized, preventive and predictive medicine. Global Multiple Biological Network Alignment (GMBNA) is one such technique, where it tries to establishes a relationship between the known and the unknown data and thereby infer knowledge among them. GMBNA, in general, is a sub-graph isomorphism problem that finds the highest degree of correlation among the given networks considering whole network. It is an NP-complete problem, in the past few years, several works have been proposed to address this issue. This paper reviews such existing frameworks for global alignment of multiple biological networks and address many aspects of GMBNA including comparison with other type of alignments, algorithmic implementation, computational challenges, dataset and species considered along with the applications of network aligners across several branches of the bioinformatics. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature