Constrained Fisher Scores Derived from Interaction Profile Hidden Markov Models Improve Protein to Protein Interaction Predictio

Protein-protein interaction plays critical roles in cellular functions. In this work, 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 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 the ROC score has shown significant improvement as compared to existing methods.

• Type: Conference
• Year: 2009
• Where: BICOB
• Authors: Alvaro J. González, Li Liao
• Keywords: BICOB 2009, Computational Biology, Interaction Profile, Protein-protein Interaction, Protein-protein Interaction Plays