Ranking Relations Using Analogies in Biological and Information Networks

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Ranking Relations Using Analogies in Biological and Information Networks

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dc.contributor.author Airoldi, Edoardo Maria
dc.contributor.author Silva, Ricardo
dc.contributor.author Ghahramani, Zoubin
dc.contributor.author Katherine Heller
dc.date.accessioned 2011-01-05T16:12:30Z
dc.date.issued 2010
dc.identifier.citation Silva, Ricardo, Katherine Heller, Zoubin Ghahramani, and Edoardo M. Airoldi. 2010. Ranking relations using analogies in biological information networks. Annals of Applied Statistics 4(2): 615-644. en_US
dc.identifier.issn 1932-6157 en_US
dc.identifier.uri http://nrs.harvard.edu/urn-3:HUL.InstRepos:4636007
dc.description.abstract Analogical reasoning depends fundamentally on the ability to learn and generalize about relations between objects. We develop an approach to rela- tional learning which, given a set of pairs of objects S = {A[super](1) : B[super](1) , A[super](2) : B[super](2),...,A[super](N) :B[super](N)}, measures how well other pairs A:B fit in with the set S. Our work addresses the following question: is the relation between objects A and B analogous to those relations found in S? Such questions are particularly relevant in information retrieval, where an investigator might want to search for analogous pairs of objects that match the query set of interest. There are many ways in which objects can be related, making the task of measuring analogies very challenging. Our approach combines a similarity measure on function spaces with Bayesian analysis to produce a ranking. It requires data containing features of the objects of interest and a link matrix specifying which relationships exist; no further attributes of such relationships are necessary. We illustrate the potential of our method on text analysis and information networks. An application on discovering functional interactions between pairs of proteins is discussed in detail, where we show that our approach can work in practice even if a small set of protein pairs is provided. en_US
dc.description.sponsorship Statistics en_US
dc.language.iso en_US en_US
dc.publisher Institute of Mathematical Statistics en_US
dc.relation.isversionof doi:10.1214/09-AOAS321 en_US
dc.relation.hasversion http://arxiv.org/abs/0912.5193/ en_US
dash.license LAA
dc.subject network analysis en_US
dc.subject Bayesian inference en_US
dc.subject variational approximation en_US
dc.subject ranking en_US
dc.subject information retrieval en_US
dc.subject data integration en_US
dc.subject Saccharomyces cerevisiae en_US
dc.title Ranking Relations Using Analogies in Biological and Information Networks en_US
dc.type Journal Article en_US
dc.description.version Version of Record en_US
dc.relation.journal Annals of Applied Statistics en_US
dash.depositing.author Airoldi, Edoardo Maria
dc.date.available 2011-01-05T16:12:30Z

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  • FAS Scholarly Articles [7501]
    Peer reviewed scholarly articles from the Faculty of Arts and Sciences of Harvard University

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