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Articles

Binary disease prediction using tail quantiles of the distribution of continuous biomarkers

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Pages 56-87 | Received 01 Apr 2021, Accepted 24 Oct 2022, Published online: 28 Nov 2022

References

  • Aguilera, A.M., Escabias, M., and Valderrama, M.J. (2006), ‘Using Principal Components for Estimating Logistic Regression with High-dimensional Multicollinear Data’, Computational Statistics & Data Analysis, 50(8), 1905–1924.
  • Berrar, D., and Flach, P. (2012), ‘Caveats and Pitfalls of Roc Analysis in Clinical Microarray Research (and How to Avoid them)’, Briefings in Bioinformatics, 13(1), 83–97.
  • Boser, B.E., Guyon, I.M., and Vapnik, V.N. (1992), ‘A Training Algorithm for Optimal Margin Classifiers’, in Proceedings of the Fifth Annual Workshop on Computational Learning Theory, pp. 144–152.
  • Boulesteix, A.-L. (2004), ‘Pls Dimension Reduction for Classification with Microarray Data’, Statistical Applications in Genetics and Molecular Biology, 3(1), 1075.
  • Bradley, A.P. (1997), ‘The Use of the Area Under the Roc Curve in the Evaluation of Machine Learning Algorithms’, Pattern Recognition, 30(7), 1145–1159.
  • Bromet, E., Andrade, L.H., Hwang, I., Sampson, N.A., Alonso, J., De Girolamo, G., De Graaf, R., Demyttenaere, K., Hu, C., Iwata, N., Karam, A.N, Kaur, J., Kostyuchenko, S., Lépine, J.-P., Levinson, D., Matschinger, H., Mora, M.E.M., Browne, M.O., Posada-Villa, J., Viana, M.C., Williams, D.R, and Kessler, R.C (2011), ‘Cross-national Epidemiology of Dsm-iv Major Depressive Episode’, BMC Medicine, 9(1), 1.
  • Calfee, C.S., Ware, L.B., Glidden, D.V., Eisner, M.D., Parsons, P.E., Thompson, B.T., and Matthay, M.A. (2011), ‘Use of Risk Reclassification with Multiple Biomarkers Improves Mortality Prediction in Acute Lung Injury’, Critical Care Medicine, 39(4), 711–717.
  • Chen, T., and Guestrin, C. (2016), ‘Xgboost: A Scalable Tree Boosting System’, in Proceedings of the 22nd ACM Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794.
  • Cook, J., and Ramadas, V. (2020), ‘When to Consult Precision-recall Curves’, The Stata Journal, 20(1), 131–148.
  • Coomans, D., and Massart, D.L. (1982), ‘Alternative K-nearest Neighbour Rules in Supervised Pattern Recognition: Part 1. K-nearest Neighbour Classification by Using Alternative Voting Rules’, Analytica Chimica Acta, 136, 15–27.
  • Cortes, C., and Vapnik, V. (1995), ‘Support-vector Networks’, Machine Learning, 20(3), 273–297.
  • De Jong, S. (1993), ‘Simpls: An Alternative Approach to Partial Least Squares Regression’, Chemometrics and Intelligent Laboratory Systems, 18(3), 251–263.
  • Everitt, B.S., Landau, S., Leese, M., and Stahl, D. (2011). Cluster analysis: Wiley series in probability and statistics, John Wiley & Sons, Chichester.
  • Filzmoser, P., Liebmann, B., and Varmuza, K. (2009), ‘Repeated Double Cross Validation’, Journal of Chemometrics, 23(4), 160–171.
  • Friedman, J. (1999), ‘Stochastic Gradient Boosting’, Technical Report, Stanford University, Department of Statistics, San Francisco, CA.
  • Friedman, J., Hastie, T., and Tibshirani, R. (2009). The elements of statistical learning: data mining, inference, and prediction, Springer, New York.
  • Friedman, J., Hastie, T., and Tibshirani, R. (2010), ‘Regularization Paths for Generalized Linear Models Via Coordinate Descent’, Journal of Statistical Software, 33(1), 1–22.
  • Halaris, A. (2013), ‘Inflammation, Heart Disease, and Depression’, Current Psychiatry Reports, 15(10), 1–9.
  • Hoerl, A.E., and Kennard, R.W. (1970), ‘Ridge Regression: Biased Estimation for Nonorthogonal Problems’, Technometrics, 12(1), 55–67.
  • Hosmer, D.W., and Lemeshow, S. (2000), 'Introduction to the logistic regression model', Applied Logistic Regression, 2, 1–30.
  • Hsu, M.-J., Chang, Y.-C.I., and Hsueh, H.-M. (2014), ‘Biomarker Selection for Medical Diagnosis Using the Partial Area Under the Roc Curve’, BMC Research Notes, 7(1), 25.
  • Japkowicz, N. (2000), ‘The Class Imbalance Problem: Significance and Strategies,’ in Proc. of the Int'l Conf. on Artificial Intelligence (Vol. 56). Citeseer, pp. 111–117.
  • Jentsch, M.C., Van Buel, E.M., Bosker, F.J., Gladkevich, A.V., Klein, H.C., Voshaar, R.C.O., Ruhé, H.G., Eisel, U.L., and Schoevers, R.A. (2015), ‘Biomarker Approaches in Major Depressive Disorder Evaluated in the Context of Current Hypotheses’, Biomarkers in Medicine, 9(3), 277–297.
  • Just, N. (2014), ‘Improving Tumour Heterogeneity Mri Assessment with Histograms’, British Journal of Cancer, 111(12), 2205–2213.
  • Ma, S., and Huang, J. (2008), ‘Penalized Feature Selection and Classification in Bioinformatics’, Briefings in Bioinformatics, 9(5), 392–403.
  • Marigheto, N., Kemsley, E., Defernez, M., and Wilson, R. (1998), ‘A Comparison of Mid-infrared and Raman Spectroscopies for the Authentication of Edible Oils’, Journal of the American Oil Chemists' Society, 75(8), 987–992.
  • Nguyen, D.V., and Rocke, D.M. (2002), ‘Tumor Classification by Partial Least Squares Using Microarray Gene Expression Data’, Bioinformatics, 18(1), 39–50.
  • Nicolaides, K. (2003), ‘Screening for Chromosomal Defects’, Ultrasound in Obstetrics & Gynecology, 21(4), 313–321.
  • Pepe, M.S., Feng, Z., Janes, H., Bossuyt, P.M., and Potter, J.D. (2008), ‘Pivotal Evaluation of the Accuracy of a Biomarker Used for Classification or Prediction: Standards for Study Design’, Journal of the National Cancer Institute, 100(20), 1432–1438.
  • Platt, J.C. (1999), ‘Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods’, in Advances in Large Margin Classifiers. MIT Press, pp. 61–74.
  • Schielen, P., van Leeuwen-Spruijt, M., Belmouden, I., Elvers, L., Jonker, M., and Loeber, J. (2006), ‘Multi-centre First-trimester Screening for Down Syndrome in the Netherlands in Routine Clinical Practice’, Prenatal Diagnosis, 26(8), 711–718.
  • Shiefa, S., Amargandhi, M., Bhupendra, J., Moulali, S., and Kristine, T. (2013), ‘First Trimester Maternal Serum Screening Using Biochemical Markers Papp-a and Free β-hcg for Down Syndrome, Patau Syndrome and Edward Syndrome’, Indian Journal of Clinical Biochemistry, 28(1), 3–12.
  • Sing, T., Sander, O., Beerenwinkel, N., and Lengauer, T. (2005), ‘Rocr: Visualizing Classifier Performance in R’, Bioinformatics, 21(20), 7881.
  • Smit, S., van Breemen, M.J., Hoefsloot, H.C., Smilde, A.K., Aerts, J.M., and De Koster, C.G. (2007), ‘Assessing the Statistical Validity of Proteomics Based Biomarkers’, Analytica Chimica Acta, 592(2), 210–217.
  • Sobocki, P., Jönsson, B., Angst, J., and Rehnberg, C. (2006), ‘Cost of Depression in Europe’, The Journal of Mental Health Policy and Economics, 9(2), 87–98.
  • Steyerberg, E.W., Vickers, A.J., Cook, N.R., Gerds, T., Gonen, M., Obuchowski, N., Pencina, M.J., and Kattan, M.W. (2010), ‘Assessing the Performance of Prediction Models: A Framework for Some Traditional and Novel Measures’, Epidemiology, 21(1), 128–138.
  • Tibshirani, R. (1996), ‘Regression Shrinkage and Selection Via the Lasso’, Journal of the Royal Statistical Society. Series B (Methodological), 58, 267–288.
  • van Buel, E.M., Meddens, M.J., Arnoldussen, E.A., van den Heuvel, E.R., Bohlmeijer, W.C., den Boer, J.A., Kobold, A.M., Boonman-de Winter, L.J., van Rumpt, D., Timmers, L.F., Veerman, M.F.A., Kamphuis, J.S., Gladkevich, A.V., Schoevers, R.A., Luiten, P.G.M., Eisel, U.L.M., Bosker, F.J., and Klein, H.C. (2019), ‘Major Depressive Disorder is Associated with Changes in a Cluster of Serum and Urine Biomarkers’, Journal of Psychosomatic Research, 125, 109796.
  • Venables, W.N., and Ripley, B.D. (2002), Modern Applied Statistics with S (4th ed.), New York: Springer.
  • Vera, L., Aceña, L., Guasch, J., Boqué, R., Mestres, M., and Busto, O. (2011), ‘Discrimination and Sensory Description of Beers Through Data Fusion’, Talanta, 87, 136–142.
  • Westerhuis, J.A., Hoefsloot, H.C., Smit, S., Vis, D.J., Smilde, A.K., van Velzen, E.J., van Duijnhoven, J.P., and van Dorsten, F.A. (2008), ‘Assessment of Plsda Cross Validation’, Metabolomics, 4(1), 81–89.
  • Wold, H. (1985), 'Partial least squares', in S Kotz, & NL Johnson, eds.,  Encyclopedia of Statistical Sciences, Wiley, New York, 581–591.
  • World Health Organization, International Programme on Chemical Safety, United Nations Environment Programme, International Labour Organisation, and Inter-Organization Programme for the Sound Management of Chemicals (IOMC). (2001), Biomarkers in Risk Assessment: Validity and Vlidation - Environmental Health Criteria 222.
  • Zhou, Z.-H. (2012), Ensemble Methods: Foundations and Algorithms, CRC Press, Boca Raton.
  • Zou, H., and Hastie, T. (2005), ‘Regularization and Variable Selection Via the Elastic Net’, Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67(2), 301–320.

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