 |
Conference papers
A new theoretical angle to semi-supervised output kernel regression for protein-protein interaction network inference
Abstract : Protein-protein interaction network inference is addressed as an output kernel learning task through semi-supervised Output Kernel Regression. Working in the framework of RKHS theory for vector-valued functions, we establish a new representer theorem devoted to semi-supervised least square regression. We then apply it to get a new model and show its relevance using numerical experiments on artificial networks and a protein-protein interaction network dataset using a very low percentage of labeled proteins in a transductive setting.
Cited literature [15 references]
https://hal.archives-ouvertes.fr/hal-00832056
Contributor : Florence d'Alché-Buc Connect in order to contact the contributor
Submitted on : Monday, June 10, 2013 - 11:08:01 AM
Last modification on : Friday, July 9, 2021 - 10:18:07 AM
Long-term archiving on: : Wednesday, September 11, 2013 - 4:11:05 AM
Contributor : Florence d'Alché-Buc Connect in order to contact the contributor
Submitted on : Monday, June 10, 2013 - 11:08:01 AM
Last modification on : Friday, July 9, 2021 - 10:18:07 AM
Long-term archiving on: : Wednesday, September 11, 2013 - 4:11:05 AM
File
mlsb_11.pdf
Files produced by the author(s)
