A statistical framework for genomic data fusion
Bioinformatics
Multiclass multiple kernel learning
Proceedings of the 24th international conference on Machine learning
lp-Norm Multiple Kernel Learning
The Journal of Machine Learning Research
Multiple Kernel Learning Algorithms
The Journal of Machine Learning Research
Non-sparse multiple kernel fisher discriminant analysis
The Journal of Machine Learning Research
Biological Sequence Classification with Multivariate String Kernels
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Analytic center cutting plane method for multiple kernel learning
Annals of Mathematics and Artificial Intelligence
| Hi-index | 0.00 |
Protein subcellular localization is a crucial ingredient to many important inferences about cellular processes, including prediction of protein function and protein interactions. While many predictive computational tools have been proposed, they tend to have complicated architectures and require many design decisions from the developer.Here we utilize the multiclass support vector machine (m-SVM) method to directly solve protein subcellular localization without resorting to the common approach of splitting the problem into several binary classification problems. We further propose a general class of protein sequence kernels which considers all motifs, including motifs with gaps. Instead of heuristically selecting one or a few kernels from this family, we utilize a recent extension of SVMs that optimizes over multiple kernels simultaneously. This way, we automatically search over families of possible amino acid motifs.We compare our automated approach to three other predictors on four different datasets, and show that we perform better than the current state of the art. Further, our method provides some insights as to which sequence motifs are most useful for determining subcellular localization, which are in agreement with biological reasoning. Data files, kernel matrices and open source software are available at http://www.fml.mpg.de/raetsch/projects/protsubloc.