Knowledge-based data analysis comes of age

  • Ochs M
  • 73

    Readers

    Mendeley users who have this article in their library.
  • 12

    Citations

    Citations of this article.

Abstract

The emergence of high-throughput technologies for measuring biological systems has introduced problems for data interpretation that must be addressed for proper inference. First, analysis techniques need to be matched to the biological system, reflecting in their mathematical structure the underlying behavior being studied. When this is not done, mathematical techniques will generate answers, but the values and reliability estimates may not accurately reflect the biology. Second, analysis approaches must address the vast excess in variables measured (e.g. transcript levels of genes) over the number of samples (e.g. tumors, time points), known as the 'large-p, small-n' problem. In large-p, small-n paradigms, standard statistical techniques generally fail, and computational learning algorithms are prone to overfit the data. Here we review the emergence of techniques that match mathematical structure to the biology, the use of integrated data and prior knowledge to guide statistical analysis, and the recent emergence of analysis approaches utilizing simple biological models. We show that novel biological insights have been gained using these techniques.

Author-supplied keywords

  • Bayesian analysis
  • Computational molecular biology
  • Databases
  • Metabolic pathways
  • Signal pathways

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document

Authors

  • Michael F. Ochs

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free