title: RANKING RELATIONS USING ANALOGIES IN BIOLOGICAL AND INFORMATION NETWORKS.
abstract: Analogical reasoning depends fundamentally on the ability to learn and generalize about relations between objects. We develop an approach to relational learning which, given a set of pairs of objectsS= {A(1):B(1),A(2):B(2), …,A(N):B(N)}, measures how well other pairsA : Bfit in with the setS. Our work addresses the following question: is the relation between objectsAandBanalogous to those relations found inS? 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.
authors.affiliation: University College London, Gower Street, London, WC1E 6BT, United Kingdom.
authors.forename: Ricardo
authors.initials: R
authors.lastname: Silva
authors.affiliation: University of Cambridge, Trumpington Street, Cambridge, CB2 1PZ, United Kingdom.
authors.forename: Katherine
authors.initials: K
authors.lastname: Heller
authors.affiliation: University of Cambridge, Trumpington Street, Cambridge, CB2 1PZ, United Kingdom.
authors.forename: Zoubin
authors.initials: Z
authors.lastname: Ghahramani
authors.affiliation: Harvard University, 1 Oxford street, Cambridge, Massachusetts 02138, USA.
authors.forename: Edoardo M
authors.initials: EM
authors.lastname: Airoldi
keywords: Bayesian inference; Network analysis; Saccharomyces cerevisiae; data integration; information retrieval; ranking; variational approximation