Inducing Features of Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to variational methods for graphical models
Proceedings of the NATO Advanced Study Institute on Learning in graphical models
A view of the EM algorithm that justifies incremental, sparse, and other variants
Proceedings of the NATO Advanced Study Institute on Learning in graphical models
Learning in graphical models
Learning Probabilistic Models of Relational Structure
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Predicting protein functions with message passing algorithms
Bioinformatics
Learning probabilistic relational models
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Loopy belief propagation for approximate inference: an empirical study
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Discriminative probabilistic models for relational data
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
A new class of upper bounds on the log partition function
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Max-margin Classification of Data with Absent Features
The Journal of Machine Learning Research
FastInf: An Efficient Approximate Inference Library
The Journal of Machine Learning Research
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Protein-protein interactions play a major role in most cellular processes. Thus, the challenge of identifying the full repertoire of interacting proteins in the cell is of great importance, and has been addressed both experimentally and computationally. Today, large scale experimental studies of interacting proteins, while partial and noisy, allow us to characterize properties of interacting proteins and develop predictive algorithms. Most existing algorithms, however, ignore possible dependencies between interacting pairs, and predict them independently of one another. In this study, we present a computational approach that overcomes this drawback by predicting protein-protein interactions simultaneously. In addition, our approach allows us to integrate various protein attributes and explicitly account for uncertainty of assay measurements. Using the language of relational Markov Random Fields, we build a unified probabilistic model that includes all of these elements. We show how we can learn our model properties efficiently and then use it to predict all unobserved interactions simultaneously. Our results show that by modeling dependencies between interactions, as well as by taking into account protein attributes and measurement noise, we achieve a more accurate description of the protein interaction network. Furthermore, our approach allows us to gain new insights into the properties of interacting proteins.