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Semantic Similarity over Gene Ontology for Multi-Label Protein Subcellular Localization

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DOI: 10.4236/eng.2013.510B014    2,262 Downloads   3,515 Views   Citations

ABSTRACT

As one of the essential topics in proteomics and molecular biology, protein subcellular localization has been extensively studied in previous decades. However, most of the methods are limited to the prediction of single-location proteins. In many studies, multi-location proteins are either not considered or assumed not existing. This paper proposes a novel multi-label subcellular-localization predictor based on the semantic similarity between Gene Ontology (GO) terms. Given a protein, the accession numbers of its homologs are obtained via BLAST search. Then, the homologous accession numbers of the protein are used as keys to search against the gene ontology annotation database to obtain a set of GO terms. The semantic similarity between GO terms is used to formulate semantic similarity vectors for classification. A support vector machine (SVM) classifier with a new decision scheme is proposed to classify the multi-label GO semantic similarity vectors. Experimental results show that the proposed multi-label predictor significantly outperforms the state-of-the-art predictors such as iLoc-Plant and Plant-mPLoc.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

Wan, S. , Mak, M. and Kung, S. (2013) Semantic Similarity over Gene Ontology for Multi-Label Protein Subcellular Localization. Engineering, 5, 68-72. doi: 10.4236/eng.2013.510B014.

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