MGOASVM: Multi-label protein subcellular localization based on gene ontology and support vector machines.

ABSTRACT:

Background

Although many computational methods have been developed to predict protein subcellular localization, most of the methods are limited to the prediction of single-location proteins. Multi-location proteins are either not considered or assumed not existing. However, proteins with multiple locations are particularly interesting because they may have special biological functions, which are essential to both basic research and drug discovery.

Results

This paper proposes an efficient multi-label predictor, namely mGOASVM, for predicting the subcellular localization of multi-location proteins. Given a protein, the accession numbers of its homologs are obtained via BLAST search. Then, the original accession number and the homologous accession numbers of the protein are use

SUBMITTER: Wan S 

PROVIDER: S-EPMC3582598 | biostudies-literature | 2012 Nov

REPOSITORIES: biostudies-literature