Generating random spanning trees more quickly than the cover time
STOC '96 Proceedings of the twenty-eighth annual ACM symposium on Theory of computing
Inferring domain-domain interactions from protein-protein interactions
Proceedings of the sixth annual international conference on Computational biology
Estimating and Improving Protein Interaction Error Rates
CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference
Kernel methods for predicting protein--protein interactions
Bioinformatics
Statistical analysis of domains in interacting protein pairs
Bioinformatics
Local modeling of global interactome networks
Bioinformatics
The relationship between Precision-Recall and ROC curves
ICML '06 Proceedings of the 23rd international conference on Machine learning
Generating random spanning trees
SFCS '89 Proceedings of the 30th Annual Symposium on Foundations of Computer Science
RECOMB 2'09 Proceedings of the 13th Annual International Conference on Research in Computational Molecular Biology
A Survey of Statistical Network Models
Foundations and Trends® in Machine Learning
Algorithms and theory of computation handbook
Reverse engineering molecular hypergraphs
Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine
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Recent advances in high-throughput experimental techniques have enabled the production of a wealth of protein interaction data, rich in both quantity and variety. While the sheer quantity and variety of data present special difficulties for modeling, they also present unique opportunities for gaining insight into protein behavior by leveraging multiple perspectives. Recent work on the modularity of protein interactions has revealed that reasoning about protein interactions at the level of domain interactions can be quite useful. We present PROCTOR, a learning algorithm for reconstructing the internal topology of protein complexes by reasoning at the domain level about both direct protein interaction data (Y2H) and protein co-complex data (AP-MS). While other methods have attempted to use data from both these kinds of assays, they usually require that cocomplex data be transformed into pairwise interaction data under a spoke or clique model, a transformation we do not require. We apply PROCTOR to data from eight highthroughput datasets, encompassing 5,925 proteins, essentially all of the yeast proteome. First we show that PROCTOR outperforms other algorithms for predicting domain-domain and protein-protein interactions from Y2H and AP-MS data. Then we show that our algorithm can reconstruct the internal topology of AP-MS purifications, revealing known complexes like Arp2/3 and RNA polymerase II, as well as suggesting new complexes along with their corresponding topologies.