Gene selection ensembles and classifier ensembles for medical diagnosis

Alon U., Barkai N., Notterman D.A., Gish K., Ybarra S., Mack D., Levine A.J. (1999): Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc Natl Acad Sci USA. 96(12): 6745–50.10.1073/pnas.96.12.67452198610359783Search in Google Scholar

Benjamini Y, Hochberg Y. (1995): Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society Series B 57: 289–300.10.1111/j.2517-6161.1995.tb02031.xSearch in Google Scholar

Breiman L. (1996): Bagging predictions. Machine Learning 24 (2): 123–140.10.1007/BF00058655Search in Google Scholar

Breiman L. (2001): Random Forests. Machine Learning 45: 5–32.10.1023/A:1010933404324Search in Google Scholar

Boulesteix A.L., Strobl C., Augustin T., Daumer M. (2008): Evaluating Microarray-based Classifiers: An Overview. Cancer Inform. 6: 77–97.10.4137/CIN.S408Search in Google Scholar

Chai H., Domeniconi C. (2004): An Evaluation of Gene Selection Methods for Multi-class Microarray Data Classification. In: Proc. 2nd European Workshop on Data Mining and Text Mining in Bioinformatics, 3–10.Search in Google Scholar

Cohen J.D., Li Y., Wang C., Thoburn B., Afsari L. et al. (2018): Detection and localization of surgically resectable cancers with a multi-analyte blood test. Science 10.1126/science.aar324710.1126/science.aar3247608030829348365Search in Google Scholar

Cohen J.D., Javed A.A, Li C., Thoburn, Wonga F., Tie J., Gibbs P. et al. (2017): Combined circulating tumor DNA and protein biomarker-based liquid biopsy for the earlier detection of pancreatic cancers. Proc Natl Acad Sci USA 114 (38): 10202–10207.10.1073/pnas.1704961114561727328874546Search in Google Scholar

Cortes C., Vapnik V. (1995): Support-Vector Networks. Machine Learning 20: 273–297.10.1007/BF00994018Search in Google Scholar

Dettling M., Bühlmann P. (2003): Boosting for tumor classification with gene expression data. Bioinformatics 19 (9): 1061–1069.10.1093/bioinformatics/btf86712801866Search in Google Scholar

Dettling M. (2004): BagBoosting for tumor classification with gene expression data. Bioinformatics: 20: 3583–3593.10.1093/bioinformatics/bth44715466910Search in Google Scholar

van Delft J.H., van Agen E., van Breda S.G., Herwijnen M.H., Staal Y.C., Kleinjans J.C. (2005): Comparison of supervised clustering methods to discriminate genotoxic from non-genotoxic carcinogens by gene expression profiling. Mutat Res, 575(1–2): 17–33.10.1016/j.mrfmmm.2005.02.006Search in Google Scholar

Ge Y., Dudoit S., Speed T.P. (2003): Resampling-based multiple testing for microarray data analysis. January 2003. Technical Report 633.Search in Google Scholar

Golub T.R., Slonim D.K., Tamayo P., Huard C., Gaasenbeek M., Mesirov J.P., Coller H., Loh M.L., Downing J.R., Caligiuri M.A., Bloomfield C.D., Lander E.S (1999): Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science, 286(5439): 531–537.10.1126/science.286.5439.531Search in Google Scholar

Guo Y., Hastie T., Tibshirani R. (2005): Regularized Discriminant Analysis and Its Application in Microarrays. Biostatistics, 1(1): 1–18.Search in Google Scholar

Hothorn T., Lausen B. (2003): Double-bagging: combining classifiers by bootstrap aggregation. Pattern Recognition 36 (2): 1303–1309.10.1016/S0031-3203(02)00169-3Search in Google Scholar

Kumarasinghe N., Tooney P.A., Schall U. (2012): Finding the needle in the haystack: A review of microarray gene expression research into schizophrenia. Australian & New Zealand Journal of Psychiatry 46 (7): 598–610.10.1177/000486741244240522441207Search in Google Scholar

van Sanden S., Lin D., Burzykowski T. (2008): Performance of gene selection and classification methods in a microarray setting: A simulation study. Communications in Statistics – Simulation and Computation 37(2): 409–424.10.1080/03610910701792554Search in Google Scholar

Skurichina M., Duin R.P.W. (2002): Bagging, Boosting and the Random Subspace Method for Linear Classifiers. Pattern Analysis & Applications 5:121–135.10.1007/s100440200011Search in Google Scholar

Tibshirani R., Hastie T., Narasimhan B., Chu G. (2002): Diagnosis of multiple cancer types by shrunken centroids of gene expression. PNAS: 99: 6567–6572.10.1073/pnas.08209929912444312011421Search in Google Scholar

Tusher V., Tibshirani R., Chu G. (2001): Significance analysis of microarrays applied to the ionizing radiation response. PNAS 98: 5116–5121.10.1073/pnas.0910624983317311309499Search in Google Scholar

Westfall P.H., Zaykin D.V., Young S.S. (2001): Multiple tests for genetic effects in association studies. In: S. Looney (ed.), Methods in Molecular Biology 184: Biostatistical Methods, Humana Press, Toloway, NJ: 143–168.10.1385/1-59259-242-2:143Search in Google Scholar

Westfall P.H., Young S.S. (1993): Resampling-based multiple testing: Examples and methods for p-value adjustment. John Wiley & Sons.Search in Google Scholar