Supervised bipartite graph inference

Yoshihiro Yamanishi

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    29 Citations (Scopus)

    Abstract

    We formulate the problem of bipartite graph inference as a supervised learning problem, and propose a new method to solve it from the viewpoint of distance metric learning. The method involves the learning of two mappings of the heterogeneous objects to a unified Euclidean space representing the network topology of the bipartite graph, where the graph is easy to infer. The algorithm can be formulated as an optimization problem in a reproducing kernel Hilbert space. We report encouraging results on the problem of compound-protein interaction network reconstruction from chemical structure data and genomic sequence data.

    Original languageEnglish
    Title of host publicationAdvances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference
    Pages1841-1848
    Number of pages8
    Publication statusPublished - Dec 1 2009
    Event22nd Annual Conference on Neural Information Processing Systems, NIPS 2008 - Vancouver, BC, Canada
    Duration: Dec 8 2008Dec 11 2008

    Publication series

    NameAdvances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference

    Other

    Other22nd Annual Conference on Neural Information Processing Systems, NIPS 2008
    CountryCanada
    CityVancouver, BC
    Period12/8/0812/11/08

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    All Science Journal Classification (ASJC) codes

    • Information Systems

    Cite this

    Yamanishi, Y. (2009). Supervised bipartite graph inference. In Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference (pp. 1841-1848). (Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference).