Boosting-based ensemble of global network aligners for PPI network alignment

The number of investigations attempting to align protein-protein interaction (PPI) networks has increased with the growth of studies focused on collecting PPI data. These works aim to identify conserved areas between species that are difficult to differentiate due to speciation. However, there is no standard approach to align PPI networks, and global aligners encounter difficulties in constructing alignments with high biological and structural quality. To address this issue, we propose an innovative ensemble technique that combines the strengths of aligners in the PPI network alignment field while avoiding their weaknesses. This approach reduces the spread of dispersion in so different individual global aligners and contributes to achieving a global standard that produces alignments of higher quality. This is possible thanks to the two branches composing our ensemble that aim to improve alignments in terms of biological or structural quality. In addition to a new heuristic replacing the second-level aligner in the biological quality-focused branch. Our approach achieves alignments of higher quality, as demonstrated through experiments with 10 different scenarios involving real data from 5 species. Our solutions outperform other individual aligners and ensemble techniques, like bagging, in terms of biological and structural quality. Moreover, the time required to perform the ensemble is minimal compared to that of individual aligners.