Genome-wide functional annotation by integrating multiple microarray datasets using meta-analysis

Authors:
Gyan Prakash Srivastava;Jing Qiu;Dong Xu
Affiliations:
Department of Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri-Columbia, 1201 E, Rollins Rd. Columbia, MO 65201, USA.;Department of Statistics, University of Missouri-Columbia, 134 I, Middlebush Hall, Columbia, MO 65201, USA.;Department of Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri-Columbia, 1201 E. Rollins Rd. Columbia, MO 65201, USA
Venue:
International Journal of Data Mining and Bioinformatics
Year:
2010

Citing 6
Cited 0

Microarray data mining: facing the challenges

ACM SIGKDD Explorations Newsletter
Understanding protein dispensability through machine-learning analysis of high-throughput data

Bioinformatics
MADE4: an R package for multivariate analysis of gene expression data

Bioinformatics
Combining multiple microarrays in the presence of controlling variables

Bioinformatics
A scalable method for integration and functional analysis of multiple microarray datasets

Bioinformatics
Functional Annotation from Meta-analysis of Microarray Datasets

BIBM '08 Proceedings of the 2008 IEEE International Conference on Bioinformatics and Biomedicine

Quantified Score

Hi-index	0.00

Visualization

Abstract

Tremendous amounts of microarray data for various organisms have provided a rich opportunity for computational analyses of gene products. Integrating these data can help inferring biological knowledge effectively. We present a new statistical method of integrating multiple microarray datasets for gene function prediction. We tested the performance of our model using yeast and human datasets. Our results show that combining multiple datasets improves the accuracy over the best function prediction of any single dataset significantly. We also compared performance of the meta p-value and meta correlation methods for function prediction. Supplementary results and code are available at http://digbio.missouri.edu/meta_analyses.