Pattern Recognition Letters
Sequence-driven features for prediction of subcellular localization of proteins
Pattern Recognition
Protein cellular localization prediction with Support Vector Machines and Decision Trees
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IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Robust prediction of protein subcellular localization combining PCA and WSVMs
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IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Protein cellular localization with multiclass support vector machines and decision trees
BSB'05 Proceedings of the 2005 Brazilian conference on Advances in Bioinformatics and Computational Biology
Protein subcellular localization prediction with associative classification and multi-class SVM
Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part II
Transactions on Computational Systems Biology II
International Journal of Bioinformatics Research and Applications
Computers in Biology and Medicine
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Motivation: Identifying the destination or localization of proteins is key to understanding their function and facilitating their purification. A number of existing computational prediction methods are based on sequence analysis. However, these methods are limited in scope, accuracy and most particularly breadth of coverage. Rather than using sequence information alone, we have explored the use of database text annotations from homologs and machine learning to substantially improve the prediction of subcellular location. Results: We have constructed five machine-learning classifiers for predicting subcellular localization of proteins from animals, plants, fungi, Gram-negative bacteria and Gram-positive bacteria, which are 81% accurate for fungi and 92--94% accurate for the other four categories. These are the most accurate subcellular predictors across the widest set of organisms ever published. Our predictors are part of the Proteome Analyst web-service. Availability:http://www.cs.ualberta.ca/~bioinfo/PA/Sub, http://www.cs.ualberta.ca/~bioinfo/PA Supplementary information: http://www.cs.ualberta.ca/~bioinfo/PA/Subcellular