References

  1. Kontijevskis A, Prusis P, Petrovska R, Yahorava S, Mutulis F, Mutule I, Komorowski J, Wikberg JES: A look inside HIV resistance through retroviral protease interaction maps.

    PloS Computational Biology 2007., 3(3):PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  2. Wikberg JES, Lapinsh M, Prusis P: Proteochemometrics: A tool for modelling the molecular interaction space. In Chemogenomics in Drug Discovery - A Medicinal Chemistry Perspective. Edited by: Kubinyi H, Müller G. Weinheim , Wiley-VCH; 2004:289-309. OpenURL

  3. Hansch C: A Quantitative Approach to Biochemical Structure-Activity Relationships.

    Accounts of Chemical Research 1969, 2:232-239. Publisher Full Text OpenURL

  4. Hvidsten TR, Wilczynski B, Kryshtafovych A, Tiuryn J, Komorowski J, Fidelis K: Discovering regulatory binding-site modules using rule-based learning.

    Genome Res 2005/06/03 edition. 2005, 15(6):856-866. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  5. van't Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AA, Mao M, Peterse HL, van der Kooy K, Marton MJ, Witteveen AT, Schreiber GJ, Kerkhoven RM, Roberts C, Linsley PS, Bernards R, Friend SH: Gene expression profiling predicts clinical outcome of breast cancer.

    Nature 2002, 415(6871):530-536. PubMed Abstract | Publisher Full Text OpenURL

  6. Johnson SR: The Trouble with QSAR (or How I Learned To Stop Worrying and Embrace Fallacy).

    J Chem Inf Model 2008, 25-26. PubMed Abstract | Publisher Full Text OpenURL

  7. Michiels S, Koscielny S, Hill C: Prediction of cancer outcome with microarrays: a multiple random validation strategy.

    The Lancet 2005, 365(9458):488-492. Publisher Full Text OpenURL

  8. Ntzani EE, Ioannidis JPA: Predictive ability of DNA microarrays for cancer outcomes and correlates: an empirical assessment.

    The Lancet 2003, 362(9394):1439-1444. Publisher Full Text OpenURL

  9. Freyhult E, Peteris P, Lapinsh M, Wikberg JES, Moulton V, Gustafsson MG: Unbiased descriptor and parameter selection confirms the potential of proteochemometric modelling.

    BMC Bioinformatics 2005, 6(50):1-14. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  10. Golbraikh A, Tropsha A: Beware of q2!

    J Mol Graph Model 2002/02/23 edition. 2002, 20(4):269-276. PubMed Abstract | Publisher Full Text OpenURL

  11. Stone M: Cross-Validatory Choice and Assessment of Statistical Predictions. In Journal of the Royal Statistical Society Series B (Methodological). Volume 36. Royal Statistical Society; 1974:111-147. OpenURL

  12. Cartmell J, Enoch S, Krstajic D, Leahy DE: Automated QSPR through Competitive Workflow.

    J Comput Aided Mol Des 2005, 19(11):821-833. PubMed Abstract | Publisher Full Text OpenURL

  13. Cartmell J, Krstajic D, Leahy DE: Competitive Workflow: novel software architecture for automating drug design.

    Curr Opin Drug Discov Devel 2007, 10(3):347-352. PubMed Abstract OpenURL

  14. Obrezanova O, Gola JM, Champness EJ, Segall MD: Automatic QSAR modeling of ADME properties: blood-brain barrier penetration and aqueous solubility.

    J Comput Aided Mol Des 2008, 22(6-7):431-440. PubMed Abstract | Publisher Full Text OpenURL

  15. Efron B, Tibshirani R: An Introduction to the Bootstrap. New York , Chapman & Hall/CRC. ; 1993. OpenURL

  16. Hastie T, Tibshirani R, Friedman J: The Elements of Statistical Learning. In Springer series in statistics. New York , Springer-Verlag; 2001:533. OpenURL

  17. Schwarz G: Estimating the Dimension of a Model. In The Annals of Statistics. Volume 6. Institute of Mathematical Statistics; 1978:461-464. Publisher Full Text OpenURL

  18. The Selwood dataset [http://www.ndsu.edu/qsar_soc/resource/datasets/selwood.htm]

    OpenURL

  19. Selwood DL, Livingstone DJ, Comley JCW, O'Dowd AB, Hudson AT, Jackson P, Jandu KS, Rose VS, Stables JN: Structure-activity relationships of antifilarial antimycin analogs: a multivariate pattern recognition study.

    Journal of Medicinal Chemistry 1990, 33(1):136-142. PubMed Abstract | Publisher Full Text OpenURL

  20. Nicolotti O, Carotti A: QSAR and QSPR studies of a highly structured physicochemical domain.

    J Chem Inf Model 2006/01/24 edition. 2006, 46(1):264-276. PubMed Abstract | Publisher Full Text OpenURL

  21. Todeschini R, Consonni V, Mauri A, Pavan M: Detecting “bad” regression models: multicriteria fitness functions in regression analysis.

    Analytica Chimica Acta 2004, 515(1):99-208. OpenURL

  22. Burman P: A Comparative Study of Ordinary Cross-Validation, v-Fold Cross-Validation and the Repeated Learning-Testing Methods. In Biometrika. Volume 76. Biometrika Trust; 1989:503-514. OpenURL

  23. Efron B: The Estimation of Prediction Error: Covariance Penalties and Cross-Validation. In Journal of the American Statistical Association. Volume 99. American Statistical Association; 2004:619-632. [Journal of the American Statistical Association] Publisher Full Text OpenURL

  24. Amaldi E, Kann V: On the Approximability of Minimizing Nonzero Variables Or Unsatisfied Relations in Linear Systems.

    Theoretical Computer Science 1997, 209:237–260. OpenURL

  25. Kubinyi H: Variable Selection in QSAR Studies. II. A Highly Efficient Combination of Systematic Search and Evolution.

    QSAR & Combinatorial Science 1994, 13(4):393-401. Publisher Full Text OpenURL

  26. Java - The Source for Java Developers [http://java.sun.com/]

    OpenURL

  27. Spjuth O, Helmus T, Willighagen EL, Kuhn S, Eklund M, Wagener J, Murray-Rust P, Steinbeck C, Wikberg JE: Bioclipse: an open source workbench for chemo- and bioinformatics.

    BMC Bioinformatics 2007/02/24 edition. 2007, 8:59. PubMed Abstract | BioMed Central Full Text | PubMed Central Full Text OpenURL

  28. P [http://www.genettasoft.com/p/P.zip]

    OpenURL

  29. JGAP - Java Genetic Algorithms Package [http://jgap.sourceforge.net/]

    OpenURL

  30. JGAPm [http://www.genettasoft.com/p/JGAPm.zip]

    OpenURL

  31. Shimodaira H: Approximately unbiased tests of regions using multistep-multiscale bootstrap resampling.

    Annals of Statistics 2004, 32:2616-2641. Publisher Full Text OpenURL

  32. pvclust [http://www.is.titech.ac.jp/~shimo/prog/pvclust/]

    OpenURL

  33. Skurichina M: Stabilizing Weak Classifiers - Regularization and Combining Techniques in Discriminant Analysis. Volume PhD. Vilnius State University; 2001. OpenURL

  34. Cho SJ, Hermsmeier MA: Genetic Algorithm Guided Selection: Variable Selection and Subset Selection.

    J Chem Inf Comput Sci 2002, 42(4):927 -9936. PubMed Abstract | Publisher Full Text OpenURL

  35. Tibshirani R: Regression Shrinkage and Selection via the Lasso. In Journal of the Royal Statistical Society Series B (Methodological). Volume 58. Royal Statistical Society; 1996:267-288. OpenURL

  36. Akaike H: A new look at the statistical model identification.

    IEEE transactions on automatic control 1974, 19(6):716 -7723. Publisher Full Text OpenURL

  37. Shao J: An asymptotic theory for linear model selection.

    Statistica Sinica 1997, 7:221-264. OpenURL

  38. Wolpert D: Stacked Generalization.

    Neural Networks, 1992, 5:241-259. Publisher Full Text OpenURL

  39. Kass RE, Wasserman L: A Reference Bayesian Test for Nested Hypotheses and its Relationship to the Schwarz Criterion. In Journal of the American Statistical Association. Volume 90. American Statistical Association; 1995:928-934. Publisher Full Text OpenURL

  40. Wasserman L: Bayesian model selection and model averaging. In Mathematical Psychology Symposium. Bloomington, Indiana ; 1999. OpenURL

  41. Kuha J: AIC and BIC - Comparisons of Assumptions and Performance.

    Sociological Methods & Research 2004, 33(2):188-229. Publisher Full Text OpenURL

  42. Hoerl AE, Kennard RW: Ridge Regression: Biased Estimation for Nonorthogonal Problems. In Technometrics. Volume 12. American Statistical Association; 1970:55-67. Publisher Full Text OpenURL

  43. Goldberg DE: Genetic Algorithms in Search, Optimization and Machine Learning. Boston , Addison-Wesley Longman Publishing Co., Inc. ; 1989:372. OpenURL