An integrated probabilistic model for functional prediction of proteins

We develop an integrated probabilistic model to combine protein physical interactions, genetic interactions, highly correlated gene expression network, protein complex data, and domain structures of individual proteins to predict protein functions. The model is an extension of our previous model for protein function prediction based on Markovian random field theory. The model is flexible in that other protein pairwise relationship information and features of individual proteins can be easily incorporated. Two features distinguish the integrated approach from other available methods for protein function prediction. One is that the integrated approach uses all available sources of information with different weights for different sources of data. It is a global approach that takes the whole network into consideration. The second feature is that the posterior probability that a protein has the function of interest is assigned. The posterior probability indicates how confident we are about...

• Type: Conference
• Year: 2003
• Where: RECOMB
• Authors: Minghua Deng, Ting Chen, Fengzhu Sun
• Keywords: Computational Biology, Integrated Approach, Protein Complex Data, Protein Function Prediction, RECOMB 2003