Functional protein representations from biological networks enable diverse cross-species inference

dc.contributor.authorFan, Jason
dc.contributor.authorCannistra, Anthony
dc.contributor.authorFried, Inbar
dc.contributor.authorLim, Tim
dc.contributor.authorSchaffner, Thomas
dc.contributor.authorCrovella, Mark
dc.contributor.authorHescott, Benjamin
dc.contributor.authorLeiserson, Mark D.M.
dc.descriptionPartial funding for Open Access provided by the UMD Libraries' Open Access Publishing Fund.
dc.description.abstractTransferring knowledge between species is key for many biological applications, but is complicated by divergent and convergent evolution. Many current approaches for this problem leverage sequence and interaction network data to transfer knowledge across species, exemplified by network alignment methods. While these techniques do well, they are limited in scope, creating metrics to address one specific problem or task. We take a different approach by creating an environment where multiple knowledge transfer tasks can be performed using the same protein representations. Specifically, our kernel-based method, MUNK, integrates sequence and network structure to create functional protein representations, embedding proteins from different species in the same vector space. First we show proteins in different species that are close in MUNKspace are functionally similar. Next,we use these representations to share knowledge of synthetic lethal interactions between species. Importantly, we find that the results using MUNK-representations are at least as accurate as existing algorithms for these tasks. Finally, we generalize the notion of a phenolog (‘orthologous phenotype’) to use functionally similar proteins (i.e. those with similar representations). We demonstrate the utility of this broadened notion by using it to identify known phenologs and novel non-obvious ones supported by current research.en_US
dc.identifier.citationJason Fan, Anthony Cannistra, Inbar Fried, Tim Lim, Thomas Schaffner, Mark Crovella, Benjamin Hescott, Mark D M Leiserson, Functional protein representations from biological networks enable diverse cross-species inference, Nucleic Acids Research, Volume 47, Issue 9, 21 May 2019, Page e51,
dc.relation.isAvailableAtCollege of Computer, Mathematical & Natural Sciencesen_us
dc.relation.isAvailableAtComputer Scienceen_us
dc.relation.isAvailableAtDigital Repository at the University of Marylanden_us
dc.relation.isAvailableAtUniversity of Maryland (College Park, MD)en_us
dc.subjectComputational Methodsen_US
dc.titleFunctional protein representations from biological networks enable diverse cross-species inferenceen_US


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