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
A bootstrapping method for learning from heterogeneous data
FGIT'11 Proceedings of the Third international conference on Future Generation Information Technology
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Transductive multi-label ensemble classification for protein function prediction
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Protein function prediction using weak-label learning
Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine
Protein function prediction by integrating multiple kernels
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Protein Function Prediction using Multi-label Ensemble Classification
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Motivation: Many algorithms that integrate multiple functional association networks for predicting gene function construct a composite network as a weighted sum of the individual networks and then use the composite network to predict gene function. The weight assigned to an individual network represents the usefulness of that network in predicting a given gene function. However, because many categories of gene function have a small number of annotations, the process of assigning these network weights is prone to overfitting. Results: Here, we address this problem by proposing a novel approach to combining multiple functional association networks. In particular, we present a method where network weights are simultaneously optimized on sets of related function categories. The method is simpler and faster than existing approaches. Further, we show that it produces composite networks with improved function prediction accuracy using five example species (yeast, mouse, fly, Esherichia coli and human). Availability: Networks and code are available from: http://morrislab.med.utoronto.ca/~sara/SW Contact:smostafavi@cs.toronto.edu; quaid.morris@utoronto.ca Supplementary information:Supplementary data are available at Bioinformatics online.