Data-driven network alignment

Abstract

In this study, we deal with the problem of biological network alignment (NA), which aims to find a node mapping between species' molecular networks that uncovers similar network regions, thus allowing for the transfer of functional knowledge between the aligned nodes. We provide evidence that current NA methods, which assume that topologically similar nodes (i.e., nodes whose network neighborhoods are isomorphic-like) have high functional relatedness, do not actually end up aligning functionally related nodes. That is, we show that the current topological similarity assumption does not hold well. Consequently, we argue that a paradigm shift is needed with how the NA problem is approached. So, we redefine NA as a data-driven framework, called TARA (data-driven NA), which attempts to learn the relationship between topological relatedness and functional relatedness without assuming that topological relatedness corresponds to topological similarity. TARA makes no assumptions about what nodes should be aligned, distinguishing it from existing NA methods. Specifically, TARA trains a classifier to predict whether two nodes from different networks are functionally related based on their network topological patterns (features). We find that TARA is able to make accurate predictions. TARA then takes each pair of nodes that are predicted as related to be part of an alignment. Like traditional NA methods, TARA uses this alignment for the across-species transfer of functional knowledge. TARA as currently implemented uses topological but not protein sequence information for functional knowledge transfer. In this context, we find that TARA outperforms existing state-of-the-art NA methods that also use topological information, WAVE and SANA, and even outperforms or complements a state-of-the-art NA method that uses both topological and sequence information, PrimAlign. Hence, adding sequence information to TARA, which is our future work, is likely to further improve its performance. The software and data are available at

Data-driven biological network alignment that uses topological, sequence, and functional information

“…We consider the exact same PPI networks of yeast (5,926 nodes and 88,779 edges) and human (15,848 nodes and 269,120 edges) that were analyzed and publicly provided by the authors of the PrimAlign study [ 20 ]. These networks were also used in the TARA study [ 5 ]. All of this allows us to fairly compare results across all of the methods.…”

“…Recently, we attempted to understand this observation [ 5 ]. Namely, we questioned the key assumption of current NA—that topologically similar nodes correspond to functionally related nodes.…”

“…Namely, we questioned the key assumption of current NA—that topologically similar nodes correspond to functionally related nodes. We found for both synthetic and PPI networks that no matter which topological similarity measure was used, the topological similarity of the functionally related nodes was barely higher than the topological similarity of the functionally unrelated nodes [ 5 ].…”

“…We named our topological relatedness-based NA framework TARA (da ta -d r iven N A ) [ 5 ]. TARA uses supervised classification to learn what topological patterns should be aligned to each other.…”

“…PrimAlign is one of very few existing integrated-within-and-across-network methods. Because PrimAlign was already shown to outperform many isolated-within-and-across-network methods [ 20 ] on the exact same data as in TARA’s evaluation [ 5 ], there was no need to evaluate TARA against any methods of that type. Importantly, TARA still outperformed PrimAlign, despite the former being a within-network-only method and hence not using any sequence information, unlike the latter.…”