Effect of training-sample size and classification difficulty on the accuracy of genomic predictors

Popovici, VladChen, WeijieGallas, Brandon G.Hatzis, ChristosShi, WeiweiSamuelson, Frank W.Nikolsky, YuriTsyganova, MarinaIshkin, AlexNikolskaya, TatianaHess, Kenneth R.Valero, VicenteBooser, DanielDelorenzi, MauroHortobagyi, Gabriel N.Shi, LemingSymmans, W. FraserPusztai, Lajos2011-12-162011-12-162011-12-16201010.1186/bcr2468https://infoscience.epfl.ch/handle/20.500.14299/75575WOS:000276986300011Introduction: As part of the MicroArray Quality Control (MAQC)-II project, this analysis examines how the choice of univariate feature-selection methods and classification algorithms may influence the performance of genomic predictors under varying degrees of prediction difficulty represented by three clinically relevant endpoints.Gene-Expression ProfilesNegative Breast-CancerPreoperative ChemotherapyTumorsCyclophosphamideFluorouracilClassifiersDoxorubicinUnivariatePaclitaxelEffect of training-sample size and classification difficulty on the accuracy of genomic predictorstext::journal::journal article::research article