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 -