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M. Zhu, L. Gao, Z. Guo, Y. Li, D. Wang, J. Wang and C. Wang, “Globally Predicting Protein Functions Based on Co-Expressed Protein-Protein Interaction Networks and Ontology Taxonomy Similarities,” Gene, Vol. 391, No. 1-2, 2007, pp. 113-119. http://dx.doi.org/10.1016/j.gene.2006.12.008

has been cited by the following article:

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

  AUTHORS: Shibiao Wan, Man-Wai Mak, Sun-Yuan Kung

  KEYWORDS: Protein Subcellular Localization; Semantic Similarity; GO Terms; Multi-Label Classification

  JOURNAL NAME: Engineering, Vol.5 No.10B, October 25, 2013

  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.