NeMoFinder: dissecting genome-wide protein-protein interactions with meso-scale network motifs
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient Detection of Network Motifs
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Predicting Protein-Protein Interactions from Protein Domains Using a Set Cover Approach
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
g-tries: an efficient data structure for discovering network motifs
Proceedings of the 2010 ACM Symposium on Applied Computing
Efficient subgraph frequency estimation with g-tries
WABI'10 Proceedings of the 10th international conference on Algorithms in bioinformatics
Labeling negative examples in supervised learning of new gene regulatory connections
CIBB'10 Proceedings of the 7th international conference on Computational intelligence methods for bioinformatics and biostatistics
Parallel discovery of network motifs
Journal of Parallel and Distributed Computing
A faster algorithm for detecting network motifs
WABI'05 Proceedings of the 5th International conference on Algorithms in Bioinformatics
An algorithm for network motif discovery in biological networks
International Journal of Data Mining and Bioinformatics
Survey: Computational challenges in systems biology
Computer Science Review
Increasing reliability of protein interactome by fast manifold embedding
Pattern Recognition Letters
G-Tries: a data structure for storing and finding subgraphs
Data Mining and Knowledge Discovery
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Motivation: High-throughput protein interaction detection methods are strongly affected by false positive and false negative results. Focused experiments are needed to complement the large-scale methods by validating previously detected interactions but it is often difficult to decide which proteins to probe as interaction partners. Developing reliable computational methods assisting this decision process is a pressing need in bioinformatics. Results: We show that we can use the conserved properties of the protein network to identify and validate interaction candidates. We apply a number of machine learning algorithms to the protein connectivity information and achieve a surprisingly good overall performance in predicting interacting proteins. Using a 'leave-one-out' approach we find average success rates between 20 and 40% for predicting the correct interaction partner of a protein. We demonstrate that the success of these methods is based on the presence of conserved interaction motifs within the network. Availability: A reference implementation and a table with candidate interacting partners for each yeast protein are available at http://www.protsuggest.org.