Improving subcellular localization prediction using text classification and the gene ontology

Citations Over TimeTop 15% of 2008 papers

Abstract

Each protein performs its functions within some specific locations in a cell. This subcellular location is important for understanding protein function and for facilitating its purification. There are now many computational techniques for predicting location based on sequence analysis and database information from homologs. A few recent techniques use text from biological abstracts: our goal is to improve the prediction accuracy of such text-based techniques. We identify three techniques for improving text-based prediction: a rule for ambiguous abstract removal, a mechanism for using synonyms from the Gene Ontology (GO) and a mechanism for using the GO hierarchy to generalize terms. We show that these three techniques can significantly improve the accuracy of protein subcellular location predictors that use text extracted from PubMed abstracts whose references are recorded in Swiss-Prot.

Related Papers

• → Assessing protein similarity with Gene Ontology and its use in subnuclear localization prediction(2006)96 cited
• → Identification of protein functions using a machine-learning approach based on sequence-derived properties(2009)52 cited
• → Multiple sequence alignments as tools for protein structure and function prediction(2003)8 cited
• Comparison of External Information Performance Predicting Subcellular Localization of Proteins(2010)