Introduction to Algorithms
Multiscale computational methods for morphogenesis and algorithms for protein-protein interaction inference
Domain-based predictive models for protein-protein interaction prediction
EURASIP Journal on Applied Signal Processing
Learning a prediction model for protein-protein recognition
Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human
Predicting protein-protein interactions using first principle methods and statistical scoring
ISB '10 Proceedings of the International Symposium on Biocomputing
Computers in Biology and Medicine
Belief propagation estimation of protein and domain interactions using the sum-product algorithm
IEEE Transactions on Information Theory - Special issue on information theory in molecular biology and neuroscience
Information Sciences: an International Journal
Mining from protein–protein interactions
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Predicting functional protein-protein interactions based on computational methods
LSMS'07 Proceedings of the 2007 international conference on Life System Modeling and Simulation
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One goal of contemporary proteome research is the elucidation of cellular protein interactions. Based on currently available protein-protein interaction and domain data, we introduce a novel method, Maximum Specificity Set Cover (MSSC), for the prediction of protein-protein interactions. In our approach, we map the relationship between interactions of proteins and their corresponding domain architectures to a generalized weighted set cover problem. The application of a greedy algorithm provides sets of domain interactions which explain the presence of protein interactions to the largest degree of specificity. Utilizing domain and protein interaction data of S. cerevisiae, MSSC enables prediction of previously unknown protein interactions, links that are well supported by a high tendency of coexpression and functional homogeneity of the corresponding proteins. Focusing on concrete examples, we show that MSSC reliably predicts protein interactions in well-studied molecular systems, such as the 26S proteasome and RNA polymerase II of S. cerevisiae. We also show that the quality of the predictions is comparable to the Maximum Likelihood Estimation while MSSC is faster. This new algorithm and all data sets used are accessible through a Web portal at http://ppi.cse.nd.edu.