Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
CLIP4: hybrid inductive machine learning algorithm that generates inequality rules
Information Sciences: an International Journal - Special issue: Soft computing data mining
Data Mining: A Knowledge Discovery Approach
Data Mining: A Knowledge Discovery Approach
Highly scalable and robust rule learner: performance evaluation and comparison
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Protein annotation from protein interaction networks and Gene Ontology
Journal of Biomedical Informatics
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Predicting protein functions is one of most challenging problems in bioinformatics. Among several approaches, such as analyzing phylogenetic profiles, homologous protein sequences or gene expression patterns, methods based on protein interaction data are very promising. We propose here a novel method using Naïve Bayes which takes advantage of protein interaction network topology to improve low-recall predictions. Our method is tested on proteins from the Human Protein Reference Database (HPRD) and on the yeast proteins from the BioGRID and compared with other state-of-the-art approaches. Analyses of the results, using several methods that include ROC analyses, indicate that our method predicts protein functions with significantly higher recall without lowering precision.