Protein function prediction using weak-label learning
Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine
A Fast Ranking Algorithm for Predicting Gene Functions in Biomolecular Networks
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Hierarchical multi-label classification using local neural networks
Journal of Computer and System Sciences
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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Gene function prediction is a complex multilabel classification problem with several distinctive features: the hierarchical relationships between functional classes, the presence of multiple sources of biomolecular data, the unbalance between positive and negative examples for each class, the complexity of the whole-ontology and genome-wide dimensions. Unlike previous works, which mostly looked at each one of these issues in isolation, we explore the interaction and potential synergy of hierarchical multilabel methods, data fusion methods, and cost-sensitive approaches on whole-ontology and genome-wide gene function prediction. Besides classical top-down hierarchical multilabel ensemble methods, in our experiments we consider two recently proposed multilabel methods: one based on the approximation of the Bayesian optimal classifier with respect to the hierarchical loss, and one based on a heuristic approach inspired by the true path rule for the biological functional ontologies. Our experiments show that key factors for the success of hierarchical ensemble methods are the integration and synergy among multilabel hierarchical, data fusion, and cost-sensitive approaches, as well as the strategy of selecting negative examples.