An eScience-Bayes strategy for analyzing omics data.

ABSTRACT:

Background

The omics fields promise to revolutionize our understanding of biology and biomedicine. However, their potential is compromised by the challenge to analyze the huge datasets produced. Analysis of omics data is plagued by the curse of dimensionality, resulting in imprecise estimates of model parameters and performance. Moreover, the integration of omics data with other data sources is difficult to shoehorn into classical statistical models. This has resulted in ad hoc approaches to address specific problems.

Results

We present a general approach to omics data analysis that alleviates these problems. By combining eScience and Bayesian methods, we retrieve scientific information and data from multiple sources and coherently incorporate them into large models. These m

SUBMITTER: Eklund M 

PROVIDER: S-EPMC2887810 | biostudies-literature | 2010 May

REPOSITORIES: biostudies-literature