Subcellular localization prediction through boosting association rules.

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 datasets show 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.