Papers and Data

  1. Golub et al. (1999) Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286: 531-37
    [Paper; Data--the second to last in the list]
  2. Dudoit S, Fridlyand J, Speed TP (2002) Comparison of discrimination methods for the classification of tumors using gene expression data. JASA 457:77-87
  3. Tusher VG, Tibshirani R, Chu G (2001) Significance analysis of microarrays applied to the ionizing radiation response. PNAS 98:5116-5121
  4. Zeng Z (1993) Theoretical Basis for Separation of Multiple Linked Gene Effects in Mapping Quantitative Trait Loci. PNAS 90:10972-6
  5. Feingold E (2002) Regression-based quantitative-trait-locus mapping in the 21st century. Am J Hum Genet 71:217-222
  6. Elston RC et al. (2000) Haseman and Elston revisited. Genet Epidemiol 19:1-17
  7. Bastone L, Reilly M, Rader DJ, Foulkes AS (2004) MDR and PRP: A comparison of methods for high-order genotype-phenotype associations. Human Heredity 58:82-92
  8. Efron B (1975) The efficiency of logistic regression compared to normal discriminant analysis. JASA 70:892:898
  9. Dempster AP, Laird NM and Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. JRSS B 39:1-38.
  10. Yap VB and Speed TP (2004) Modeling DNA base substitution in large genomic regions from two organisms. J Mol Evol 58:12-18.
  11. Hwang DG, Green P (2004) Bayesian Markov chain Monte Carlo sequence analysis reveals neutral substitution patterns in mammalian evolution. PNAS 39:13994-14001
  12. Yi N and Xu S (2001) Bayesian mapping of quantitative trait loci under complicated mating designs. Genetics 157:1759-1771
  13. Tibshirani RJ and Efron B (2002) Pre-validation and inference in microarrays. Statistical Applications in Genetics and Molecular Biology. Issue 1. Vol 1. [link]
  14. Green PJ (1995) Reversible Jump Markov Chain Monte Carlo Computation and Bayesian Model Determination. Biometrika 82:711-732 [JSTOR link]
  15. Bureau A, Dupuis J, Falls K, Lunetta KL, Hayward B, Keith TP, Van Eerdewegh P. (2005) Identifying SNPs predictive of phenotype using random forests. Genet Epidemiol. 28:171-82.
  16. Kuo WP et al. (2004) A primer on gene expression and microarrays for machine learning reserachers. J Biomed Inform 37:293-303. [Online]

Readings on Computing

  1. Fox J (2002) Nonparametric Regression. Appendix to An R and Splus Companion to Applied Regression [Online]
  2. Weisberg S (2005) Computing primer for applied linear regression, 3rd edition, using R and S-plus [Online]
  3. A tutorial on Support Vector Machine (SVM) from www.support-vector.net
  4. On random forest, "Expository Notes" from Leo Breiman from http://www.stat.berkeley.edu/users/breiman/RandomForests/cc_home.htm.

Misc.

  1. Errata on "The elements of statistical learning" (cumulative tile the 4th printing) [Online]