TY - GEN

T1 - Supervised bipartite graph inference

AU - Yamanishi, Yoshihiro

PY - 2009

Y1 - 2009

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84858775082&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84858775082&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84858775082

SN - 9781605609492

T3 - Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference

SP - 1841

EP - 1848

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

T2 - 22nd Annual Conference on Neural Information Processing Systems, NIPS 2008

Y2 - 8 December 2008 through 11 December 2008

ER -