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• W2067585755 abstract "Computational methods for predicting protein subcellular localization have used various types of features, including N-terminal sorting signals, amino acid compositions, and text annotations from protein databases. Our approach does not use biological knowledge such as the sorting signals or homologues, but use just protein sequence information. The method divides a protein sequence into short k-mer sequence fragments which can be mapped to word features in document classification. A large number of class association rules are mined from the protein sequence examples that range from the N-terminus to the C-terminus. Then, a boosting algorithm is applied to those rules to build up a final classifier. Experimental results using benchmark data sets show that our method is excellent in terms of both the classification performance and the test coverage. The result also implies that the k-mer sequence features which determine subcellular locations do not necessarily exist in specific positions of a protein sequence. Online prediction service implementing our method is available at http://isoft.postech.ac.kr/research/BCAR/subcell." @default.
• W2067585755 created "2016-06-24" @default.
• W2067585755 creator A5026344117 @default.
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• W2067585755 date "2012-03-01" @default.
• W2067585755 modified "2023-09-26" @default.
• W2067585755 title "Subcellular Localization Prediction through Boosting Association Rules" @default.
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• W2067585755 doi "https://doi.org/10.1109/tcbb.2011.131" @default.
• W2067585755 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/21968957" @default.
• W2067585755 hasPublicationYear "2012" @default.
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