@inproceedings{5256,
  title = {An Automated Combination of Kernels for Predicting Protein Subcellular Localization},
  journal = {Algorithms in Bioinformatics: 8th International Workshop (WABI 2008)},
  booktitle = {WABI 2008},
  abstract = {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 ocalization, which are in agreement with biological
  reasoning.},
  pages = {186-197},
  editors = {Crandall, K. A., J. Lagergren},
  publisher = {Springer},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Berlin, Germany},
  month = sep,
  year = {2008},
  author = {Ong, CS. and Zien, A.},
  doi = {10.1007/978-3-540-87361-7_16},
  month_numeric = {9}
}