EURASIP Journal on Bioinformatics and Systems Biology
Mining of Protein Subcellular Localizations based on a Syntactic Dependency Tree and WordNet
Proceedings of the 2008 conference on Knowledge-Based Software Engineering: Proceedings of the Eighth Joint Conference on Knowledge-Based Software Engineering
Prediction of protein sub-cellular localization using information from texts and sequences
BioNLP '08 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Learning cellular sorting pathways using protein interactions and sequence motifs
RECOMB'11 Proceedings of the 15th Annual international conference on Research in computational molecular biology
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)
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Motivation: Knowing the localization of a protein within the cell helps elucidate its role in biological processes, its function and its potential as a drug target. Thus, subcellular localization prediction is an active research area. Numerous localization prediction systems are described in the literature; some focus on specific localizations or organisms, while others attempt to cover a wide range of localizations. Results: We introduce SherLoc, a new comprehensive system for predicting the localization of eukaryotic proteins. It integrates several types of sequence and text-based features. While applying the widely used support vector machines (SVMs), SherLoc’s main novelty lies in the way in which it selects its text sources and features, and integrates those with sequence-based features. We test SherLoc on previously used datasets, as well as on a new set devised specifically to test its predictive power, and show that SherLoc consistently improves on previous reported results. We also report the results of applying SherLoc to a large set of yet-unlocalized proteins. Availability: SherLoc, along with Supplementary Information, is available at: http://www-bs.informatik.uni-tuebingen.de/Services/SherLoc/ Contact: shatkay@cs.queensu.ca Supplementary information: Supplementary data are available at Bioinformatics online.