Team MODBIO

Members
Overall Objectives
Scientific Foundations
Application Domains
Software
New Results
Other Grants and Activities
Dissemination
Bibliography

Section: New Results

Keywords : Statistical learning theory, semi-supervised learning, bio-informatics.

Semi-supervised learning; application to the disulfide bridges prediction

Participant : François Denis.

Semi-supervised learning algorithms aimed to exploit simultaneously labeled and unlabeled data for classification. We have been working for several years on a specific semi-supervised learning problem: binary classification from positive and unlabeled data. Theoretical results, strengthened by experimental results, have proved that many learning algorithm can be adapted to this context (see [16] ). With Christophe Magnan, who is doing a PhD on this subject at the LIF, we are currently studying applications of this paradigm to a biological problem: disulfide bridges prediction [28] . We are also working, with Liva Ralaivola (MdC, Université de Provence), on a more sophisticated model in order to deal with contact maps in proteins.


previous
next

Logo Inria