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  ). 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  . 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.