Knowledge-based data analysis comes of age

M. F. Ochs
2009 Briefings in Bioinformatics  
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
more » ... ches 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. Michael Ochs has been trained in astrophysics with a focus on the structure of quasars. His present research at Johns Hopkins University is on the use of Bayesian statistical approaches and computational modeling in cancer research.
doi:10.1093/bib/bbp044 pmid:19854753 pmcid:PMC3700349 fatcat:takbpogtrnfmfbc6v2cazhfffy