Markov random field modeling in computer vision
Markov random field modeling in computer vision
Gene functional classification from heterogeneous data
RECOMB '01 Proceedings of the fifth annual international conference on Computational biology
Integrative Analysis of Protein Interaction Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Prediction of Protein Function Using Protein-Protein Interaction Data
CSB '02 Proceedings of the IEEE Computer Society Conference on Bioinformatics
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Protein functional class prediction with a combined graph
Expert Systems with Applications: An International Journal
Artificial Intelligence in Medicine
Multi-view prediction of protein function
Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine
Exploiting label dependency for hierarchical multi-label classification
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
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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 assigning the function to the protein. We apply our integrated approach to predict functions of yeast proteins based upon MIPS protein function classifications and upon the interaction networks based on MIPS physical and genetic interactions, gene expression profiles, Tandem Affinity Purification (TAP) protein complex data, and protein domain information. We study the sensitivity and specificity of the integrated approach using different sources of information by the leave-one-out approach. In contrast to using MIPS physical interactions only, the integrated approach combining all of the information increases the sensitivity from 57% to 87% when the specificity is set at 57%-an increase of 30%. It should also be noted that enlarging the interaction network greatly increases the number of proteins whose functions can be predicted.