Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/26005
Title: Semantic similarity over gene ontology for multi-label protein subcellular localization
Authors: Wan, S
Mak, MW 
Kung, SY
Keywords: Protein Subcellular Localization
Semantic Similarity
GO Terms
Multi-Label Classification
Issue Date: 2013
Publisher: Scientific Research Publishing
Source: Engineering, 2013, v. 5, no. 10, p. 68-72 How to cite?
Journal: Engineering 
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 acces- sion 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 se- mantic 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.
URI: http://hdl.handle.net/10397/26005
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

Page view(s)

44
Last Week
0
Last month
Citations as of Apr 22, 2018

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.