Name
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 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. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-540-87361-7_16 | WABI |
Keywords | Field | DocType |
protein function,binary classification problem,automated combination,sequence motif,protein subcellular localization,predicting protein subcellular localization,biological reasoning,protein sequence kernel,protein interaction,subcellular localization,common approach,automated approach,binary classification,protein sequence,support vector machine,amino acid | Binary classification,Protein sequencing,Matrix (mathematics),Computer science,Artificial intelligence,Kernel (linear algebra),Heuristic,Pattern recognition,Support vector machine,Multiple kernel learning,Bioinformatics,Machine learning,Subcellular localization | Conference |
Volume | ISSN | Citations |
5251 | 0302-9743 | 14 |
PageRank | References | Authors |
0.65 | 19 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cheng Soon Ong | 1 | 1232 | 86.27 |
| Alexander Zien | 2 | 1255 | 146.93 |