Domain-based predictive models for protein-protein interaction prediction
EURASIP Journal on Applied Signal Processing
Inferring protein-protein interaction networks from protein complex data
International Journal of Bioinformatics Research and Applications
International Journal of Data Mining and Bioinformatics
Large-scale Protein-Protein Interaction prediction using novel kernel methods
International Journal of Data Mining and Bioinformatics
Evolutionary Optimization of Kernel Weights Improves Protein Complex Comembership Prediction
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Brief communication: Effect of example weights on prediction of protein-protein interactions
Computational Biology and Chemistry
Inferring protein interactions from sequence using support vector machine
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Prediction of protein protein interactions from primary sequences
International Journal of Data Mining and Bioinformatics
Image ranking with implicit feedback from eye movements
Proceedings of the 2010 Symposium on Eye-Tracking Research & Applications
Reconstructing the topology of protein complexes
RECOMB'07 Proceedings of the 11th annual international conference on Research in computational molecular biology
MP-PIPE: a massively parallel protein-protein interaction prediction engine
Proceedings of the international conference on Supercomputing
Collective graph identification
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Similarity boosting for label noise tolerance in protein-chemical interaction prediction
Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine
ICCSA'05 Proceedings of the 2005 international conference on Computational Science and Its Applications - Volume Part III
Prediction of human proteins interacting with human papillomavirus proteins
ICIC'11 Proceedings of the 7th international conference on Intelligent Computing: bio-inspired computing and applications
RECOMB'12 Proceedings of the 16th Annual international conference on Research in Computational Molecular Biology
Mathematical and Computer Modelling: An International Journal
Review: Supervised classification and mathematical optimization
Computers and Operations Research
Mining from protein–protein interactions
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Domain information based prediction of protein-protein interactions of glucosinolate biosynthesis
International Journal of Computer Applications in Technology
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Motivation: Proteome-wide prediction of protein--protein interaction is a difficult and important problem in biology. Although there have been recent advances in both experimental and computational methods for predicting protein--protein interactions, we are only beginning to see a confluence of these techniques. In this paper, we describe a very general, high-throughput method for predicting protein--protein interactions. Our method combines a sequence-based description of proteins with experimental information that can be gathered from any type of protein--protein interaction screen. The method uses a novel description of interacting proteins by extending the signature descriptor, which has demonstrated success in predicting peptide/protein binding interactions for individual proteins. This descriptor is extended to protein pairs by taking signature products. The signature product is implemented within a support vector machine classifier as a kernel function. Results: We have applied our method to publicly available yeast, Helicobacter pylori, human and mouse datasets. We used the yeast and H.pylori datasets to verify the predictive ability of our method, achieving from 70 to 80% accuracy rates using 10-fold cross-validation. We used the human and mouse datasets to demonstrate that our method is capable of cross-species prediction. Finally, we reused the yeast dataset to explore the ability of our algorithm to predict domains. Contact:smartin@sandia.gov.