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pubmed-article:16076885pubmed:abstractTextMOTIVATION: Sequence similarity often suggests evolutionary relationships between protein sequences that can be important for inferring similarity of structure or function. The most widely-used pairwise sequence comparison algorithms for homology detection, such as BLAST and PSI-BLAST, often fail to detect less conserved remotely-related targets. RESULTS: In this paper, we propose a new general graph-based propagation algorithm called MotifProp to detect more subtle similarity relationships than pairwise comparison methods. MotifProp is based on a protein-motif network, in which edges connect proteins and the k-mer based motif features that they contain. We show that our new motif-based propagation algorithm can improve the ranking results over a base algorithm, such as PSI-BLAST, that is used to initialize the ranking. Despite the complex structure of the protein-motif network, MotifProp can be easily interpreted using the top-ranked motifs and motif-rich regions induced by the propagation, both of which are helpful for discovering conserved structural components in remote homologies.lld:pubmed
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pubmed-article:16076885pubmed:authorpubmed-author:NobleWilliam...lld:pubmed
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pubmed-article:16076885pubmed:dateRevised2007-11-14lld:pubmed
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pubmed-article:16076885pubmed:year2005lld:pubmed
pubmed-article:16076885pubmed:articleTitleMotif-based protein ranking by network propagation.lld:pubmed
pubmed-article:16076885pubmed:affiliationDepartment of Computer Science, Columbia University New York, NY 10027, USA.lld:pubmed
pubmed-article:16076885pubmed:publicationTypeJournal Articlelld:pubmed
pubmed-article:16076885pubmed:publicationTypeResearch Support, U.S. Gov't, P.H.S.lld:pubmed
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pubmed-article:16076885pubmed:publicationTypeEvaluation Studieslld:pubmed
pubmed-article:16076885pubmed:publicationTypeResearch Support, N.I.H., Extramurallld:pubmed
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