by Tao Xu, Linfang Du, Yan Zhou
Abstract:
BACKGROUND: Researchers interested in analysing the expression patterns of functionally related genes usually hope to improve the accuracy of their results beyond the boundaries of currently available experimental data. Gene ontology (GO) data provides a novel way to measure the functional relationship between gene products. Many approaches have been reported for calculating the similarities between two GO terms, known as semantic similarities. However, biologists are more interested in the relationship between gene products than in the scores linking the GO terms. To highlight the relationships among genes, recent studies have focused on functional similarities. RESULTS: In this study, we evaluated five functional similarity methods using both protein-protein interaction (PPI) and expression data of S. cerevisiae. The receiver operating characteristics (ROC) and correlation coefficient analysis of these methods showed that the maximum method outperformed the other methods. Statistical comparison of multiple- and single-term annotated proteins in biological process ontology indicated that genes with multiple GO terms may be more reliable for separating true positives from noise. CONCLUSION: This study demonstrated the reliability of current approaches that elevate the similarity of GO terms to the similarity of proteins. Suggestions for further improvements in functional similarity analysis are also provided.
Reference:
Evaluation of GO-based functional similarity measures using S. cerevisiae protein interaction and expression profile data. (Tao Xu, Linfang Du, Yan Zhou), In BMC Bioinformatics, volume 9, 2008.
Bibtex Entry:
@article{Xu2008,
abstract = {BACKGROUND: Researchers interested in analysing the expression patterns of functionally related genes usually hope to improve the accuracy of their results beyond the boundaries of currently available experimental data. Gene ontology (GO) data provides a novel way to measure the functional relationship between gene products. Many approaches have been reported for calculating the similarities between two GO terms, known as semantic similarities. However, biologists are more interested in the relationship between gene products than in the scores linking the GO terms. To highlight the relationships among genes, recent studies have focused on functional similarities. RESULTS: In this study, we evaluated five functional similarity methods using both protein-protein interaction (PPI) and expression data of S. cerevisiae. The receiver operating characteristics (ROC) and correlation coefficient analysis of these methods showed that the maximum method outperformed the other methods. Statistical comparison of multiple- and single-term annotated proteins in biological process ontology indicated that genes with multiple GO terms may be more reliable for separating true positives from noise. CONCLUSION: This study demonstrated the reliability of current approaches that elevate the similarity of GO terms to the similarity of proteins. Suggestions for further improvements in functional similarity analysis are also provided.},
author = {Xu, Tao and Du, Linfang and Zhou, Yan},
doi = {10.1186/1471-2105-9-472},
issn = {1471-2105},
journal = {BMC Bioinformatics},
keywords = {Algorithms,Artificial Intelligence,Computational Biology,Computational Biology: methods,Controlled,Databases,Information Storage and Retrieval,Information Storage and Retrieval: methods,Oligonucleotide Array Sequence Analysis,Protein,Protein Interaction Mapping,ROC Curve,SML-LIB-BIBLIO,Saccharomyces cerevisiae,Saccharomyces cerevisiae Proteins,Saccharomyces cerevisiae Proteins: chemistry,Saccharomyces cerevisiae Proteins: genetics,Saccharomyces cerevisiae Proteins: metabolism,Saccharomyces cerevisiae: genetics,Saccharomyces cerevisiae: metabolism,Structure-Activity Relationship,Vocabulary,lang:ENG},
mendeley-tags = {SML-LIB-BIBLIO,lang:ENG},
month = jan,
number = {1},
pages = {472},
pmid = {18986551},
title = {{Evaluation of GO-based functional similarity measures using S. cerevisiae protein interaction and expression profile data.}},
url = {http://www.biomedcentral.com/1471-2105/9/472},
volume = {9},
year = {2008}
}