Biological network alignment (NA) aims to find a node mapping between
species' molecular networks that uncovers similar network regions, thus
allowing for transfer of functional knowledge between the aligned nodes.
However, current NA methods do not end up aligning functionally related nodes.
A likely reason is that they assume it is topologically similar nodes that are
functionally related. However, we show that this assumption does not hold well.
So, a paradigm shift is needed with how the NA problem is approached. We
redefine NA as a data-driven framework, TARA (daTA-dRiven network Alignment),
which attempts to learn the relationship between topological relatedness and
functional relatedness without assuming that topological relatedness
corresponds to topological similarity, like traditional NA methods do. TARA
trains a classifier to predict whether two nodes from different networks are
functionally related based on their network topological patterns. 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. Clearly, TARA as currently implemented uses topological but not
protein sequence information for this task. 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.

TARA, a data-driven framework for biological network alignment, utilizes a classifier to predict functional relatedness based on network topological patterns and outperforms existing state-of-the-art methods.

基于数据的网络对齐

Data-driven network alignment

Biological network alignment aims to identify similar regions between
networks of different species. Existing methods compute node "similarities" to
rapidly identify from possible alignments the "high-scoring" alignments with
respect to the overall node similarity. However, the accuracy of the alignments
is then evaluated with some other measure that is different than the node
similarity used to construct the alignments. Typically, one measures the amount
of conserved edges. Thus, the existing methods align similar nodes between
networks hoping to conserve many edges (after the alignment is constructed!).
Instead, we introduce MAGNA to directly "optimize" edge conservation while
the alignment is constructed. MAGNA uses a genetic algorithm and our novel
function for crossover of two "parent" alignments into a superior "child"
alignment to simulate a "population" of alignments that "evolves" over time;
the "fittest" alignments survive and proceed to the next "generation", until
the alignment accuracy cannot be optimized further. While we optimize our new
and superior measure of the amount of conserved edges, MAGNA can optimize any
alignment accuracy measure. In systematic evaluations against existing
state-of-the-art methods (IsoRank, MI-GRAAL, and GHOST), MAGNA improves
alignment accuracy of all methods.

本文提出了一种名为 MAGNA 的新方法，使用基因算法和交叉功能来优化边保留，同时优化任何对齐精度度量，与现有的先进方法比较，MAGNA 提高了所有方法的对齐精度.