HetNetAligner: A Novel Algorithm for Local Alignment of Heterogeneous Biological Networks

The importance of the use of networks to model and analyse biological data and the interplay of bio-molecules is widely recognised. Consequently, many algorithms for the analysis and the comparison of networks (such as alignment algorithms) have been developed in the past. Recently, many different approaches tried to integrate into a single model the interplay of different molecules, such as genes, transcription factors and microRNAs. A possible formalism to model such scenario comes from node/edge coloured networks (or heterogeneous networks) implemented as node/ edge-coloured graphs. Consequently, the need for the introduction of alignment algorithms able to analyse heterogeneous networks arises. We here focus on the local comparison of heterogeneous networks that may be formulated as a network alignment problem. To the best of our knowledge, this problem has not been investigated in the past. We here propose HetNetAligner a novel algorithm that receives as input two heterogeneous networks (node-coloured graphs) and a similarity function among nodes of two networks. We first build a single alignment graph. Then we mine this graph extracting relevant subgraphs. We also implemented our algorithm, and we tested it on some selected heterogeneous biological networks. Preliminary results confirm that our method builds high-quality alignments. The website https://sites.google.com/view/heterogeneusnetworkalignment/home contains supplementary material and the code.