Protein-protein network inference with regularized output and input kernel methods

Lecturer : 
Florence d'Alché Buc
Event type: 
HIIT seminar
Event time: 
2011-02-11 10:15 to 11:00
Kumpula Exactum C222
Talk announcement:
Combined Guest Lecture / HIIT Seminar Kumpula, Friday Feb 11 10:15, Exactum C222

Florence d'Alché Buc
IBISC, Université d'Evry-Val d'Essonne, Evry, France

Protein-protein network inference with regularized output and input  
kernel methods

Prediction of a physical interaction between two proteins has been  
addressed in the context of supervised learning, unsupervised  
learning and more recently, semi-supervised learning using various  
sources of information (genomic, phylogenetic, protein localization  
and function). The problem can be seen as a kernel matrix completion  
task if one defines a kernel that encodes similarity between  
proteins as nodes in a graph or alternatively, as a binary  
supervised classification task where inputs are pairs of proteins.
In this talk, we first make a review of existing works (matrix  
completion, SVM for pairs, metric learning, training set expansion),  
identifying the relevant features of each approach. Then we define  
the framework of output kernel regression (OKR) that uses the kernel  
trick in the output feature space and we develop a new family of  
methods based on Kernel Ridge Regression that benefit from the use  
of kernels both in the input feature space and the output feature  
space. The main interest of such methods is that imposing various  
regularization constraints still leads to closed form solutions. We  
show especially how such an approach allows to handle unlabeled data  
in a transductive setting of the network inference problem.
New results on simulated data and yeast data illustrate the talk.

Joint work with Céline Brouard and Marie Szafranski.

--Matti Järvisalo

Last updated on 3 Feb 2011 by Matti Järvisalo - Page created on 3 Feb 2011 by Matti Järvisalo