A note on Platt's probabilistic outputs for support vector machines
Machine Learning
Bioinformatics
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Prediction of Protein Functions with Gene Ontology and Interspecies Protein Homology Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
True Path Rule Hierarchical Ensembles for Genome-Wide Gene Function Prediction
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
COSNet: a cost sensitive neural network for semi-supervised learning in graphs
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
Large scale ranking and repositioning of drugs with respect to drugbank therapeutic categories
ISBRA'12 Proceedings of the 8th international conference on Bioinformatics Research and Applications
Network-Based Drug Ranking and Repositioning with Respect to DrugBank Therapeutic Categories
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Ranking genes in functional networks according to a specific biological function is a challenging task raising relevant performance and computational complexity problems. To cope with both these problems we developed a transductive gene ranking method based on kernelized score functions able to fully exploit the topology and the graph structure of biomolecular networks and to capture significant functional relationships between genes. We run the method on a network constructed by integrating multiple biomolecular data sources in the yeast model organism, achieving significantly better results than the compared state-of-the-art network-based algorithms for gene function prediction, and with relevant savings in computational time. The proposed approach is general and fast enough to be in perspective applied to other relevant node ranking problems in large and complex biological networks.