Advanced search
1 file | 363.82 KB

Gene expression trends and protein features effectively complement each other in gene function prediction

(2009) Bioinformatics. 25(3). p.322-330
Author
Organization
Abstract
Motivation: Genome-scale 'omics' data constitute a potentially rich source of information about biological systems and their function. There is a plethora of tools and methods available to mine omics data. However, the diversity and complexity of different omics data types is a stumbling block for multi-data integration, hence there is a dire need for additional methods to exploit potential synergy from integrated orthogonal data. Rough Sets provide an efficient means to use complex information in classification approaches. Here, we set out to explore the possibilities of Rough Sets to incorporate diverse information sources in a functional classification of unknown genes. Results: We explored the use of Rough Sets for a novel data integration strategy where gene expression data, protein features and Gene Ontology (GO) annotations were combined to describe general and biologically relevant patterns represented by If-Then rules. The descriptive rules were used to predict the function of unknown genes in Arabidopsis thaliana and Schizosaccharomyces pombe. The If-Then rule models showed success rates of up to 0.89 (discriminative and predictive power for both modeled organisms); whereas, models built solely of one data type (protein features or gene expression data) yielded success rates varying from 0.68 to 0.78. Our models were applied to generate classifications for many unknown genes, of which a sizeable number were confirmed either by PubMed literature reports or electronically interfered annotations. Finally, we studied cell cycle protein-protein interactions derived from both tandem affinity purification experiments and in silico experiments in the BioGRID interactome database and found strong experimental evidence for the predictions generated by our models. The results show that our approach can be used to build very robust models that create synergy from integrating gene expression data and protein features.
Keywords
NETWORK, IDENTIFICATION, MICROARRAY, YEAST, ONTOLOGY, BIOLOGICAL PROCESS, CELL-CYCLE, ARABIDOPSIS, PATTERNS, SYSTEM

Downloads

  • (...).pdf
    • full text
    • |
    • UGent only
    • |
    • PDF
    • |
    • 363.82 KB

Citation

Please use this url to cite or link to this publication:

Chicago
Wabnik, Krzysztof, Torgeir Hvidsten, Anna Kedzierska, Jelle Van Leene, Geert De Jaeger, Gerrit Beemster, Jan Komorowski, and Martin Kuiper. 2009. “Gene Expression Trends and Protein Features Effectively Complement Each Other in Gene Function Prediction.” Bioinformatics 25 (3): 322–330.
APA
Wabnik, K., Hvidsten, T., Kedzierska, A., Van Leene, J., De Jaeger, G., Beemster, G., Komorowski, J., et al. (2009). Gene expression trends and protein features effectively complement each other in gene function prediction. Bioinformatics, 25(3), 322–330.
Vancouver
1.
Wabnik K, Hvidsten T, Kedzierska A, Van Leene J, De Jaeger G, Beemster G, et al. Gene expression trends and protein features effectively complement each other in gene function prediction. Bioinformatics. 2009;25(3):322–30.
MLA
Wabnik, Krzysztof, Torgeir Hvidsten, Anna Kedzierska, et al. “Gene Expression Trends and Protein Features Effectively Complement Each Other in Gene Function Prediction.” Bioinformatics 25.3 (2009): 322–330. Print.
@article{528653,
  abstract     = {Motivation: Genome-scale 'omics' data constitute a potentially rich source of information about biological systems and their function. There is a plethora of tools and methods available to mine omics data. However, the diversity and complexity of different omics data types is a stumbling block for multi-data integration, hence there is a dire need for additional methods to exploit potential synergy from integrated orthogonal data. Rough Sets provide an efficient means to use complex information in classification approaches. Here, we set out to explore the possibilities of Rough Sets to incorporate diverse information sources in a functional classification of unknown genes.

Results: We explored the use of Rough Sets for a novel data integration strategy where gene expression data, protein features and Gene Ontology (GO) annotations were combined to describe general and biologically relevant patterns represented by If-Then rules. The descriptive rules were used to predict the function of unknown genes in Arabidopsis thaliana and Schizosaccharomyces pombe. The If-Then rule models showed success rates of up to 0.89 (discriminative and predictive power for both modeled organisms); whereas, models built solely of one data type (protein features or gene expression data) yielded success rates varying from 0.68 to 0.78. Our models were applied to generate classifications for many unknown genes, of which a sizeable number were confirmed either by PubMed literature reports or electronically interfered annotations. Finally, we studied cell cycle protein-protein interactions derived from both tandem affinity purification experiments and in silico experiments in the BioGRID interactome database and found strong experimental evidence for the predictions generated by our models. The results show that our approach can be used to build very robust models that create synergy from integrating gene expression data and protein features.},
  author       = {Wabnik, Krzysztof and Hvidsten, Torgeir and Kedzierska, Anna and Van Leene, Jelle and De Jaeger, Geert and Beemster, Gerrit and Komorowski, Jan and Kuiper, Martin},
  issn         = {1367-4803},
  journal      = {Bioinformatics},
  keyword      = {NETWORK,IDENTIFICATION,MICROARRAY,YEAST,ONTOLOGY,BIOLOGICAL PROCESS,CELL-CYCLE,ARABIDOPSIS,PATTERNS,SYSTEM},
  language     = {eng},
  number       = {3},
  pages        = {322--330},
  title        = {Gene expression trends and protein features effectively complement each other in gene function prediction},
  url          = {http://dx.doi.org/10.1093/bioinformatics/btn625},
  volume       = {25},
  year         = {2009},
}

Altmetric
View in Altmetric
Web of Science
Times cited: