Network alignment (NA) aims to find a node mapping between compared networks that uncovers regions of high topological similarity , thus allowing for the transfer of functional knowledge between  aligned nodes. For example, one can align protein-protein interaction networks of yeast and human and infer functions of human proteins based on functions of their yeast counterparts. However, many current NA methods do not end up aligning functionally related nodes. A likely reason is that current NA methods assume that topologically similar nodes have high functional relatedness . However, in this study we provide evidence that this assumption does not hold well. As such , 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 network Alignment), 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  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 the uses this alignment for the across-species transfer of functional knowledge. In this context, we find that TARA outperforms or complements existing state-of-the-art NA methods WAVE, SANA, and PrimAlign .
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 .
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Network
Biological network
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compared
species' molecular
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regions of high topological similarity
similar network regions
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the
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the
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For example, one can align protein-protein interaction networks of yeast and human and infer functions of human proteins based on functions of their yeast counterparts. However, many
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current NA methods assume that
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have high functional relatedness
that are functionally related
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in this study we provide evidence
we show
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As such
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So, we
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called
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. TARA makes no assumptions about what nodes should be aligned, distinguishing it from existing NA methods . Specifically,
, like traditional NA methods do.
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(features)
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that
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the
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In this context, we
Clearly, TARA as currently implemented uses topological but not protein sequence information for this task. We
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or complements
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WAVE,
that also use topological information, WAVE and
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PrimAlign
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