Convex Optimization
Fast String Kernels using Inexact Matching for Protein Sequences
The Journal of Machine Learning Research
A statistical framework for genomic data fusion
Bioinformatics
Semi-supervised protein classification using cluster kernels
Bioinformatics
Fast protein classification with multiple networks
Bioinformatics
Unified video annotation via multigraph learning
IEEE Transactions on Circuits and Systems for Video Technology
On multiple kernel learning with multiple labels
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Transductive multi-label ensemble classification for protein function prediction
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Transductive Multilabel Learning via Label Set Propagation
IEEE Transactions on Knowledge and Data Engineering
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Determining protein function constitutes an exercise in integrating information derived from several heterogeneous high-throughput experiments. To utilize the information spread across multiple sources in a combined fashion, these data sources are transformed into kernels. Several protein function prediction methods follow a two-phased approach: they first optimize the weights on individual kernels to produce a composite kernel, and then train a classifier on the composite kernel. As such, these methods result in an optimal composite kernel, but not necessarily in an optimal classifier. On the other hand, some methods optimize the loss of binary classifiers, and learn weights for the different kernels iteratively. A protein has multiple functions, and each function can be viewed as a label. These methods solve the problem of optimizing weights on the input kernels for each of the labels. This is computationally expensive and ignores inter-label correlations. In this paper, we propose a method called Protein Function Prediction by Integrating Multiple Kernels (ProMK). ProMK iteratively optimizes the phases of learning optimal weights and reducing the empirical loss of a multi-label classifier for each of the labels simultaneously, using a combined objective function. ProMK can assign larger weights to smooth kernels and downgrade the weights on noisy kernels. We evaluate the ability of ProMK to predict the function of proteins using several standard benchmarks. We show that our approach performs better than previously proposed protein function prediction approaches that integrate data from multiple networks, and multi-label multiple kernel learning methods.