S G Baker

Summary

Affiliation: National Institutes of Health
Country: USA

Publications

  1. Baker S, Kramer B. Identifying genes that contribute most to good classification in microarrays. BMC Bioinformatics. 2006;7:407 pubmed
    ..Using multiple random validation, investigators should look for classification rules that perform well with few genes and select, for further study, genes with relatively high frequencies of occurrence in these classification rules. ..
  2. Baker S, Kramer B. Paradoxes in carcinogenesis: new opportunities for research directions. BMC Cancer. 2007;7:151 pubmed
    ..Now we have some hope of making progress." The same viewpoint should apply to cancer research. It is easy to ignore this piece of wisdom about the means to advance knowledge, but we do so at our peril. ..
  3. Baker S, Soto A, Sonnenschein C, Cappuccio A, Potter J, Kramer B. Plausibility of stromal initiation of epithelial cancers without a mutation in the epithelium: a computer simulation of morphostats. BMC Cancer. 2009;9:89 pubmed publisher
    ..Because the model is biologically plausible, we hope that these results will stimulate further experiments. ..
  4. 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. ..
  5. 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. ..
  6. 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. ..
  7. 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. ..

Locale

Detail Information

Publications7

  1. Baker S, Kramer B. Identifying genes that contribute most to good classification in microarrays. BMC Bioinformatics. 2006;7:407 pubmed
    ..Using multiple random validation, investigators should look for classification rules that perform well with few genes and select, for further study, genes with relatively high frequencies of occurrence in these classification rules. ..
  2. Baker S, Kramer B. Paradoxes in carcinogenesis: new opportunities for research directions. BMC Cancer. 2007;7:151 pubmed
    ..Now we have some hope of making progress." The same viewpoint should apply to cancer research. It is easy to ignore this piece of wisdom about the means to advance knowledge, but we do so at our peril. ..
  3. Baker S, Soto A, Sonnenschein C, Cappuccio A, Potter J, Kramer B. Plausibility of stromal initiation of epithelial cancers without a mutation in the epithelium: a computer simulation of morphostats. BMC Cancer. 2009;9:89 pubmed publisher
    ..Because the model is biologically plausible, we hope that these results will stimulate further experiments. ..
  4. 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. ..
  5. 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. ..
  6. 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. ..
  7. 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. ..