TableS3. Performance metrics of 92 machine learning algorithm combinations evaluated in the LOOCV framework. ID GSE75010 GSE25906 GSE10588 GSE24129 Lasso+SVM 0.923175606 0.839045093 0.894673688 0.869585157 Stepglm[backward]+RF 0.917085645 0.812519894 0.840222323 0.847449923 Lasso+RF 0.881830129 0.825782493 0.84669658 0.844638903 glmBoost+Enet[alpha=0.2] 0.856923077 0.841697613 0.837962337 0.825503881 glmBoost+Ridge 0.855897436 0.844350133 0.830309714 0.824027115 Stepglm[both]+RF 0.876722892 0.815172414 0.822391841 0.84018267 Lasso+LDA 0.862564103 0.841697613 0.805583576 0.835405428 glmBoost+plsRglm 0.842051282 0.849655172 0.82266292 0.827374707 RF 0.88189622 0.809867374 0.824204382 0.817008619 glmBoost+Enet[alpha=0.3] 0.86 0.825782493 0.82275236 0.82247094 glmBoost+Enet[alpha=0.1] 0.856410256 0.841697613 0.791980659 0.837941254 Lasso+plsRglm 0.838461538 0.844350133 0.8211224 0.822555843 glmBoost+Enet[alpha=0.4] 0.860512821 0.823129973 0.825015376 0.813978811 glmBoost+Enet[alpha=0.6] 0.86 0.815172414 0.824201407 0.822133141 Lasso+Stepglm[both] 0.858461538 0.815172414 0.832172989 0.815350927 RF+Enet[alpha=0.1] 0.85025641 0.831087533 0.824487726 0.812986547 RF+Ridge 0.818461538 0.860265252 0.796250545 0.839440795 glmBoost+Enet[alpha=0.7] 0.847841805 0.825782493 0.814199339 0.817130201 Lasso+Stepglm[forward] 0.804615385 0.831087533 0.832511505 0.827372053 glmBoost+Stepglm[backward] 0.858461538 0.815172414 0.808638663 0.811774626 Enet[alpha=0.7] 0.862051282 0.809867374 0.821612081 0.800383696 RF+glmBoost 0.856410256 0.815172414 0.805218033 0.815398224 glmBoost 0.857948718 0.823129973 0.800805734 0.808890606 RF+Enet[alpha=0.4] 0.859487179 0.801909814 0.813481617 0.809847591 Enet[alpha=0.1] 0.846153846 0.823129973 0.799897403 0.815506887 RF+Enet[alpha=0.2] 0.855384615 0.815172414 0.805036927 0.807954158 Enet[alpha=0.5] 0.857948718 0.812519894 0.812408768 0.799210351 Ridge 0.796410256 0.848687922 0.793078885 0.842070463 Enet[alpha=0.3] 0.856923077 0.817824934 0.797666589 0.806931999 Enet[alpha=0.2] 0.853846154 0.812519894 0.80303092 0.809556145 glmBoost+Stepglm[both] 0.858461538 0.815172414 0.792561697 0.808246351 Stepglm[both]+Enet[alpha=0.9] 0.729230769 0.899518394 0.805129821 0.840471708 RF+Enet[alpha=0.5] 0.860512821 0.799257294 0.799222377 0.813075386 Enet[alpha=0.6] 0.859487179 0.799257294 0.8050924 0.806407352 RF+Enet[alpha=0.3] 0.858974359 0.804562334 0.80679999 0.799629403 glmBoost+Enet[alpha=0.5] 0.804615385 0.817824934 0.822679449 0.818316074 Enet[alpha=0.4] 0.853846154 0.796604775 0.810809879 0.797508412 Lasso 0.857948718 0.791299735 0.814357626 0.791518783 Enet[alpha=0.8] 0.861025641 0.796604775 0.794664694 0.793814751 glmBoost+Enet[alpha=0.9] 0.844637968 0.809867374 0.790826735 0.795220208 RF+Enet[alpha=0.6] 0.803076923 0.801909814 0.823403112 0.799744292 Lasso+glmBoost 0.86 0.788647215 0.791044971 0.787491645 glmBoost+Lasso 0.846848774 0.804562334 0.764997603 0.805970589 Stepglm[both]+plsRglm 0.824102564 0.816171652 0.805966584 0.857076331 glmBoost+Enet[alpha=0.8] 0.817421797 0.815172414 0.777917304 0.792535761 Enet[alpha=0.9] 0.804102564 0.801909814 0.789427897 0.797922445 RF+Enet[alpha=0.8] 0.803076923 0.793952255 0.803164866 0.791146492 glmBoost+Stepglm[forward] 0.805128205 0.799257294 0.781061142 0.803256157 Stepglm[both]+Enet[alpha=0.8] 0.829230769 0.812505627 0.800258025 0.846652238 Stepglm[backward]+Enet[alpha=0.2] 0.833846154 0.871536121 0.748865735 0.831526487 Stepglm[both]+Enet[alpha=0.4] 0.833846154 0.835761009 0.779596847 0.835679055 RF+Enet[alpha=0.7] 0.832589744 0.799257294 0.780151854 0.800498593 Stepglm[backward]+Enet[alpha=0.7] 0.832820513 0.815946432 0.792649347 0.832709167 RF+Lasso 0.832059829 0.788647215 0.797145348 0.780932079 Stepglm[backward]+glmBoost 0.831547009 0.865861505 0.750225736 0.814437895 Stepglm[both]+Enet[alpha=0.7] 0.831034188 0.796158918 0.792795306 0.835992121 Stepglm[both]+Ridge 0.830521368 0.78858828 0.775485199 0.865987589 glmBoost+RF 0.830008547 0.762122016 0.782914556 0.76735928 Stepglm[backward]+Enet[alpha=0.5] 0.831794872 0.823690652 0.772125158 0.826826296 RF+Enet[alpha=0.9] 0.812469091 0.791299735 0.769022885 0.781304438 RF+Stepglm[forward] 0.827013191 0.760795756 0.785035824 0.777696162 Stepglm[backward]+plsRglm 0.802410256 0.79230655 0.766420761 0.867488542 Lasso+Stepglm[backward] 0.847692308 0.764774536 0.766031635 0.770685338 Stepglm[both]+Enet[alpha=0.6] 0.834871795 0.811278366 0.76115881 0.837460539 glmBoost+LDA 0.829854152 0.780689655 0.753184268 0.784692037 Stepglm[backward]+Enet[alpha=0.8] 0.824836509 0.801019183 0.767621994 0.836260199 Stepglm[both]+glmBoost 0.819818866 0.790837839 0.783615035 0.823525438 Stepglm[backward]+Enet[alpha=0.1] 0.814801223 0.779270072 0.787793662 0.834840982 Stepglm[both]+Enet[alpha=0.5] 0.80978358 0.804205115 0.767408922 0.825712016 glmBoost+SVM 0.804765937 0.762122016 0.760246291 0.757710704 Stepglm[both]+Enet[alpha=0.1] 0.799748294 0.749443369 0.78991217 0.84719719 Stepglm[backward]+Enet[alpha=0.4] 0.794730651 0.791846445 0.752012665 0.837120929 Stepglm[backward]+Ridge 0.789713008 0.763331778 0.765855602 0.846136257 Stepglm[backward]+Enet[alpha=0.3] 0.784695365 0.729669396 0.794083373 0.837111852 Stepglm[backward]+Enet[alpha=0.9] 0.779677722 0.777912032 0.751752053 0.831870273 RF+SVM 0.8 0.762122016 0.763754669 0.759638067 RF+LDA 0.852123261 0.693156499 0.787235264 0.734198591 Stepglm[both]+Enet[alpha=0.3] 0.734871795 0.714649876 0.781581791 0.835531383 plsRglm 0.816410256 0.754164456 0.729057777 0.761198281 Stepglm[backward]+Enet[alpha=0.6] 0.730769231 0.720112448 0.767311814 0.829116632 Stepglm[both]+Enet[alpha=0.2] 0.732820513 0.698854212 0.780672007 0.834266247 Stepglm[backward]+SVM 0.733333333 0.796604775 0.744097172 0.768148043 Stepglm[backward] 0.853046405 0.675915119 0.760395752 0.725292968 Stepglm[both]+SVM 0.71025641 0.796604775 0.719903497 0.753631024 SVM 0.725641026 0.779363395 0.717463518 0.751618973 Stepglm[forward] 0.741025642 0.67193634 0.7253751 0.702201865 LDA 0.756410258 0.613580902 0.736929081 0.67689968 RF+Stepglm[backward] 0.746164993 0.675915119 0.737925027 0.694122194 RF+Stepglm[both] 0.735919728 0.675915119 0.716322796 0.703272175 Stepglm[both]+LDA 0.725674463 0.697665782 0.717828751 0.643308651 Stepglm[backward]+LDA 0.715429198 0.697665782 0.6984183 0.651267754 RF+plsRglm 0.705183933 0.657267905 0.658652688 0.594435209