Knowledge-based data analysis comes of age

Michael F. Ochs

Research output: Contribution to journalArticle

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 estimatesmay 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.

Original languageEnglish (US)
Article numberbbp044
Pages (from-to)30-39
Number of pages10
JournalBriefings in Bioinformatics
Volume11
Issue number1
DOIs
StatePublished - Oct 23 2009

Keywords

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

ASJC Scopus subject areas

  • Molecular Biology
  • Information Systems

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