Performing Local Network Alignment by Ensembling Global Aligners

Interactions among proteins are important mechanisms in living cells. The whole set of interactions is often referred to as a protein-protein interaction network (PIN). Comparison among such networks may discover conserved (or disrupted) patterns of interactions among species. Such comparison is performed using network alignment algorithms. They help analyse PPI networks for a better understanding of biological processes such as finding conserved regions between species, giving us insight into their evolution. However, there is no best aligner or standard evaluation measure to assess the quality of alignments. In this work, we use several aligners to produce an ensembled result, which can further improve individual aligners' alignment quality. Two basic ensemble approaches are used: One by finding majority node mappings from aligners and another by combining their results into one final alignment. These alignments are then evaluated based on three scoring schemes: Gene Ontology Consistency (GOC), Node Coverage (NCV) and Generalised Symmetric Substructure Score (GS(3)) using IsoBase PPI networks. Results show that the majority voting based ensemble scheme performs well in GS(3) while the ensemble by the union of the decision by different aligners produces satisfactory outcomes in comparison in GOC and NCV scores.