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dc.contributor.authorLeiserson, Mark D. M.en_US
dc.contributor.authorFan, Jasonen_US
dc.contributor.authorCannistra, Anthonyen_US
dc.contributor.authorFried, Inbaren_US
dc.contributor.authorLim, Timen_US
dc.contributor.authorSchaffner, Thomasen_US
dc.contributor.authorCrovella, Marken_US
dc.contributor.authorHescott, Benjaminen_US
dc.date.accessioned2020-05-04T14:58:16Z
dc.date.available2020-05-04T14:58:16Z
dc.date.issued2018-04
dc.identifier.citationMark DM Leiserson, Jason Fan, Anthony Cannistra, Inbar Fried, Tim Lim, Thomas Schaffner, Mark Crovella, Benjamin Hescott. 2018. "A Multi-Species Functional Embedding Integrating Sequence and Network Structure." Proceedings of RECOMB. RECOMB. https://doi.org/10.1101/229211
dc.identifier.urihttps://hdl.handle.net/2144/40525
dc.description.abstractA key challenge to transferring knowledge between species is that different species have fundamentally different genetic architectures. Initial computational approaches to transfer knowledge across species have relied on measures of heredity such as genetic homology, but these approaches suffer from limitations. First, only a small subset of genes have homologs, limiting the amount of knowledge that can be transferred, and second, genes change or repurpose functions, complicating the transfer of knowledge. Many approaches address this problem by expanding the notion of homology by leveraging high-throughput genomic and proteomic measurements, such as through network alignment. In this work, we take a new approach to transferring knowledge across species by expanding the notion of homology through explicit measures of functional similarity between proteins in different species. Specifically, our kernel-based method, HANDL (Homology Assessment across Networks using Diffusion and Landmarks), integrates sequence and network structure to create a functional embedding in which proteins from different species are embedded in the same vector space. We show that inner products in this space and the vectors themselves capture functional similarity across species, and are useful for a variety of functional tasks. We perform the first whole-genome method for predicting phenologs, generating many that were previously identified, but also predicting new phenologs supported from the biological literature. We also demonstrate the HANDL embedding captures pairwise gene function, in that gene pairs with synthetic lethal interactions are significantly separated in HANDL space, and the direction of separation is conserved across species. Software for the HANDL algorithm is available at http://bit.ly/lrgr-handl.en_US
dc.language.isoen_US
dc.relation.ispartofProceedings of Research in Computational Molecular Biology
dc.subjectScience & technologyen_US
dc.subjectLife sciences & biomedicineen_US
dc.subjectTechnologyen_US
dc.subjectBiochemical research methodsen_US
dc.subjectComputer science, information systemsen_US
dc.subjectMathematical & computational biologyen_US
dc.subjectBiochemistry & molecular biologyen_US
dc.subjectComputer scienceen_US
dc.subjectArtificial intelligence & image processingen_US
dc.titleA multi-species functional embedding integrating sequence and network structureen_US
dc.typeConference materialsen_US
dc.description.versionPublished versionen_US
dc.identifier.doi10.1101/229211
pubs.elements-sourcemanual-entryen_US
pubs.notesCode for this method is available at link below extra1: Github repository extra1url: https://github.com/theJasonFan/HANDL catid1: Other catid2: bioinfen_US
pubs.notesEmbargo: No embargoen_US
pubs.organisational-groupBoston Universityen_US
pubs.organisational-groupBoston University, College of Arts & Sciencesen_US
pubs.organisational-groupBoston University, College of Arts & Sciences, Department of Computer Scienceen_US
pubs.publication-statusPublisheden_US
dc.identifier.orcid0000-0002-5005-7019 (Crovella, Mark)
dc.identifier.mycv297306


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