S G Baker
Affiliation: National Institutes of Health
- Baker S. Simple and flexible classification of gene expression microarrays via Swirls and Ripples. BMC Bioinformatics. 2010;11:452 pubmed publisher..The parsimonious selection of classifiers coupled with the selection of either Swirls or Ripples provides a good basis for formulating a simple, yet flexible, classification rule. Open source software is available for download. ..
- Baker S, Kramer B. Surrogate endpoint analysis: an exercise in extrapolation. J Natl Cancer Inst. 2013;105:316-20 pubmed publisher..In summary, when using surrogate endpoint analyses, an appreciation of the problems of extrapolation is crucial. ..
- Baker S, Bonetti M. Evaluating Markers for Guiding Treatment. J Natl Cancer Inst. 2016;108: pubmed publisher..Because the method has desirable decision-analytic properties and yields an informative plot, it is worth applying to randomized trials on the chance there is a large treatment effect in a subgroup determined by the predictive markers. ..
- Baker S. Comparative Analysis of Biologically Relevant Response Curves in Gene Expression Experiments: Heteromorphy, Heterochrony, and Heterometry. Microarrays (Basel). 2014;3:39-51 pubmed publisher..We illustrated the algorithm using data on gene expression at 14 times in the embryonic development in two species of frogs. Software written in Mathematica is freely available. ..