Using the largest PPI networks from BioGRID, we show that L-GRAAL's alignments outperform other network alignments: they uncover the largest common sub-networks between aligned networks, as measured by EC and symmetric sub-structure scores.
Protein structure alignment is often modeled as the largest common point set (LCP) problem based on the Root Mean Square Deviation (RMSD), a measure commonly used to evaluate structural similarity.
Following Kuchaiev's work (Kuchaiev et al., 2010), we evaluate network alignment quality by five measures including edge correctness (EC), largest common connected subgraph (LCCS), symmetric substructure score (S 3 ), functional consistency (FC) and average of functional similarity (AFS).
The main problem we consider is the largest common point set (LCP) problem under the RMSD, a well-known problem in protein structure alignment.
The latter was done by computing the pairwise alignments of the pathways in each cluster (in a predetermined order) and by considering their common set of aligned reactions, that is, the intersection of their largest common motif.
Only one point is emphasized here: the modern world is in an era of common alignment.
In a second approach, we considered the above mentioned diversity of JH agonist structures and split their common alignment into two separated alignments.
Sequences from each strain were combined into a multiple sequence alignment via their common alignment to the S288c genome.
Finally, we use the size of the largest connected component (LCC) to ensure that the alignments correspond to large common connected sub-structure, instead of several small disconnected ones (Kuchaiev et al., 2010).
Blin et al. [ 13] proposed a greedy method based on algorithms for global alignment that first finds a set of anchors and then recursively match genes found in large common intervals.
