Making large-scale support vector machine learning practical
Advances in kernel methods
Exploiting generative models in discriminative classifiers
Proceedings of the 1998 conference on Advances in neural information processing systems II
Discriminating Transmembrane Proteins From Signal Peptides Using SVM-Fisher Approach
ICMLA '05 Proceedings of the Fourth International Conference on Machine Learning and Applications
| Hi-index | 0.00 |
Protein-protein interaction plays critical roles in cellular functions. In this paper, we propose a computational method to predict protein-protein interaction by using support vector machines and the constrained Fisher scores derived from interaction profile hidden Markov models (ipHMM) that characterize domains involved in the interaction. The constrained Fisher scores are obtained as the gradient, with respect to the model parameters, of the posterior probability for the protein to be aligned with the ipHMM as conditioned on a specified path through the model state space, in this case we used the most probable path ---as determined by the Viterbi algorithm. The method is tested by leave-one-out cross validation experiments with a set of interacting protein pairs adopted from the 3DID database. The prediction accuracy measured by ROC score has shown significant improvement as compared to the previous methods.