Multiclass multiple kernel learning
Proceedings of the 24th international conference on Machine learning
Is subcellular localization informative for modeling protein-protein interaction signal?
Research Letters in Signal Processing
An Automated Combination of Kernels for Predicting Protein Subcellular Localization
WABI '08 Proceedings of the 8th international workshop on Algorithms in Bioinformatics
It's about time: Signal recognition in staged models of protein translocation
Pattern Recognition
Using decision templates to predict subcellular localization of protein
PRIB'07 Proceedings of the 2nd IAPR international conference on Pattern recognition in bioinformatics
In search of protein locations
BioNLP '11 Proceedings of BioNLP 2011 Workshop
Subcellular Localization Prediction through Boosting Association Rules
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Feature subset selection for protein subcellular localization prediction
ICIC'06 Proceedings of the 2006 international conference on Computational Intelligence and Bioinformatics - Volume Part III
International Journal of Data Mining and Bioinformatics
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Motivation: Functional annotation of unknown proteins is a major goal in proteomics. A key annotation is the prediction of a protein's subcellular localization. Numerous prediction techniques have been developed, typically focusing on a single underlying biological aspect or predicting a subset of all possible localizations. An important step is taken towards emulating the protein sorting process by capturing and bringing together biologically relevant information, and addressing the clear need to improve prediction accuracy and localization coverage. Results: Here we present a novel SVM-based approach for predicting subcellular localization, which integrates N-terminal targeting sequences, amino acid composition and protein sequence motifs. We show how this approach improves the prediction based on N-terminal targeting sequences, by comparing our method TargetLoc against existing methods. Furthermore, MultiLoc performs considerably better than comparable methods predicting all major eukaryotic subcellular localizations, and shows better or comparable results to methods that are specialized on fewer localizations or for one organism. Availability: http://www-bs.informatik.uni-tuebingen.de/Services/MultiLoc/ Contact: hoeglund@informatik.uni-tuebingen.de