Classifying genes to the correct Gene Ontology Slim term in Saccharomyces cerevisiae using neighbouring genes with classification learning Metadata
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- Main Title Classifying genes to the correct Gene Ontology Slim term in Saccharomyces cerevisiae using neighbouring genes with classification learning
Author: Amthauer, Heather A.Creator Type: PersonalCreator Info: Frostburg State University
Author: Tsatsoulis, C. (Costas), 1962-Creator Type: PersonalCreator Info: University of North Texas
Name: BioMed Central Ltd.Place of Publication: [London, United Kingdom]
- Creation: 2010-05-28
- Content Description: Article discussing research on classifying genes to the correct gene ontology slim term in Saccharomyces cerevisiae using neighbouring genes with classification learning.
- Physical Description: 9 p.
- Keyword: Gene Ontology Slim
- Keyword: genes
- Keyword: Saccharomyces cerevisiae
- Journal: BMC Genomics, 2010, London: BioMed Central Ltd.
- Publication Title: BMC Genomics
- Volume: 11
- Issue: 340
- Peer Reviewed: True
Name: UNT Scholarly WorksCode: UNTSW
Name: UNT College of EngineeringCode: UNTCOE
- Rights Access: public
- Rights License: by
- DOI: 10.1186/1471-2164-11-340
- Archival Resource Key: ark:/67531/metadc122144
- Academic Department: Computer Science and Engineering
- Display Note: Abstract: Background: There is increasing evidence that gene location and surrounding genes influence the functionality of genes in the eukaryotic genome. Knowing the Gene Ontology Slim terms associated with a gene gives the authors insight into a gene's functionality by informing the authors how its gene product behaves in a cellular context using three different ontologies: molecular function, biological process, and cellular component. In this study, the authors analyzed if they could classify a gene in Saccharomyces cerevisiae to its correct Gene Ontology Slim term using information about its location in the genome and information from its nearest-neighbouring genes using classification learning. Results: The authors performed experiments to establish that the MultiBoostAB algorithm using the J48 classifier could correctly classify Gene Ontology Slim terms of a gene given information regarding the gene's location and information from its nearest-neighbouring genes for training. Different neighbourhood sizes were examined to determine how many nearest neighbours should be included around each gene to provide better classification rules. The authors' results show that by just incorporating neighbour information from each gene's two-nearest neighbours, the percentage of correctly classified genes to their correct Gene Ontology Slim term for each ontology reaches over 80% with high accuracy (reflected in F-measures over 0.80) of the classification rules produced. Conclusions: The authors confirmed that in classifying genes to their correct Gene Ontology Slim term, the inclusion of neighbour information from those genes is beneficial. Knowing the location of a gene and the Gene Ontology Slim information from neighbouring genes gives us insight into that gene's functionality. This benefit is seen by just including information from a gene's two-nearest neighbouring genes.