Information Processing and Management: an International Journal
Protein complex prediction via cost-based clustering
Bioinformatics
Annotating proteins by mining protein interaction networks
Bioinformatics
Measuring semantic similarity between Gene Ontology terms
Data & Knowledge Engineering
Multiple Graph Alignment for the Structural Analysis of Protein Active Sites
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Gene Ontology-Based Annotation Analysis and Categorization of Metabolic Pathways
SSDBM '07 Proceedings of the 19th International Conference on Scientific and Statistical Database Management
Functional annotation of regulatory pathways
Bioinformatics
Mining globally distributed frequent subgraphs in a single labeled graph
Data & Knowledge Engineering
Molecular Function Prediction Using Neighborhood Features
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Aligning biomolecular networks using modular graph kernels
WABI'09 Proceedings of the 9th international conference on Algorithms in bioinformatics
IEEE Transactions on Information Technology in Biomedicine
Disease gene prioritization based on topological similarity in protein-protein interaction networks
RECOMB'11 Proceedings of the 15th Annual international conference on Research in computational molecular biology
Predicting Protein Function by Multi-Label Correlated Semi-Supervised Learning
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Protein annotation from protein interaction networks and Gene Ontology
Journal of Biomedical Informatics
Predicting Protein Functions from Protein Interaction Networks
International Journal of Knowledge Discovery in Bioinformatics
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In this paper, we propose a pattern-based protein function annotation framework, employing protein interaction networks, to predict annotation functions of proteins. More specifically, we first detect patterns that appear in the neighborhood of proteins with a particular functionality, and then transfer annotations between two proteins only if they have similar annotation patterns. We show that, in comparison with other techniques, our approach predicts protein annotations more effectively. Our technique (a) produces the highest prediction accuracy of 70-80% precision and recall for different organism specific datasets, and (b) is robust to false positives in protein interaction networks.