The tridimensional structure of a protein is constrained or stabilized by some local interactions between distant residues of the protein, such as disulfide bonds, electrostatic interactions or hydrogen links. The in silico prediction of the disulfide connectivity has been widely studied: most results were based on few amino-acids around bonded cysteines, which we call local environments of cysteines. In order to evaluate the impact of such local information onto residue pairing, we propose a machine learning based protocol, independent from the type of contact, to detect affinities between local environments which would contribute to residues pairing. Finally, we experiment our protocol on proteins that feature disulfide or salt bridges. The results show that local environments contribute to the formation of salt bridges. However, results on disulfide bridges are not significantly positive with the class of linear functions used by the perceptron-type algorithm we propose.

We address the problem of efficiently learning Naive Bayes classifiers under class-conditional classification noise (CCCN). Naive Bayes classifiers rely on the hypothesis that the distributions associated to each class are product distributions. When data is subject to CCC-noise, these conditional distributions are themselves mixtures of product distributions. We give analytical formulas which makes it possible to identify them from data subject to CCCN. Then, we design a learning algorithm based on these formulas able to learn Naive Bayes classifiers under CCCN. We present results on artificial datasets and datasets extracted from the UCI repository database. These results show that CCCN can be efficiently and successfully handled.

We address the issue of the learnability of concept classes under three classification noise models in the probably approximately correct framework. After introducing the Class-Conditional Classification Noise (CCCN) model, we investigate the problem of the learnability of concept classes under this particular setting and we show that concept classes that are learnable under the well-known uniform classification noise (CN) setting are also CCCN-learnable, which gives CN = CCCN. We then use this result to prove the equality between the set of concept classes that are CN-learnable and the set of concept classes that are learnable in the Constant Partition Classification Noise (CPCN) setting, or, in other words, we show that CN = CPCN.