HetNetAligner: Design and Implementation of an algorithm for heterogeneous network alignment on Apache Spark

TL;DR

HetNetAligner is a novel framework built on Apache Spark for aligning heterogeneous biological networks, enabling efficient analysis of complex multi-molecule interactions and extracting relevant biological insights.

Contribution

It introduces a two-step alignment algorithm for heterogeneous networks and implements it on Spark to handle large biological data efficiently.

Findings

Reduces computational time for network alignment.

Successfully extracts relevant biological knowledge.

Handles large heterogeneous networks effectively.

Abstract

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 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. To the best of our knowledge, all the existing algorithms are not able to mine heterogeneous networks. We propose a two-step alignment strategy that receives as input two heterogeneous networks (node-coloured graphs) and a similarity function among nodes of two networks extending the previous formulations. We first build a single alignment graph. Then we mine this graph extracting relevant subgraphs. Despite this simple approach, the analysis of such networks relies on graph and subgraph isomorphism and the size of the data is still growing. Therefore the use of high-performance data analytics framework is needed. We here present HetNetAligner a framework built on top of Apache Spark. We also implemented our algorithm, and we tested it on some selected heterogeneous biological networks. Preliminary results confirm that our method may extract relevant knowledge from biological data reducing the computational time.