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Table 3 Model Performance

From: A systematic analysis of genomics-based modeling approaches for prediction of drug response to cytotoxic chemotherapies

 

NLSVR

PCR

LNSVR

ANN

NFS

DEG

LIM

NFS

DEG

LIM

NFS

DEG

LIM

NFS

DEG

LIM

BLM

.207

.202

.202

.239

.208

.208

.151

.1

.209

.147

.17

.21

BTZ

.38

.404

.365

.422

.399

.354

.332

.326

.232

−.009

.299

.24

CIS

.05

.08

N/A

−.009

.047

N/A

.03

.079

N/A

−.066

.034

N/A

CYT

.313

.32

.279

.32

.281

.256

.337

.291

.269

.226

.266

.291

DTX

.422

.44

.408

.367

.409

.382

.357

.319

.359

.185

.318

.207

DOX

.273

.27

.117

.243

.285

.106

.27

.226

.103

.115

.173

.096

ETP

.289

.302

.294

.248

.291

.263

.238

.219

.273

.209

.195

.246

GEM

.143

.139

.166

.153

.117

.143

.07

.063

.165

.131

.119

.134

MTX

.461

.455

.462

.431

.435

.433

.417

.388

.338

.411

.391

.322

MMC

.237

.302

.244

.264

.269

.25

.27

.224

.239

.203

.153

.248

PTX

.32

.27

.198

.287

.282

.159

.233

.170

.191

−.106

.211

.177

VBL

.44

.403

.399

.408

.398

.37

.398

.339

.371

.112

.302

.363

VOR

.509

.495

.486

.5

.487

.439

.484

.471

.404

.445

.42

.42

SN-38

.383

.417

.409

.379

.391

.443

.397

.404

.429

.01

.327

.402

5-FU

.463

.464

.40

.455

.484

.354

.451

.438

.337

.309

.409

.365

AVG

.326

.331

.316

.314

.319

.297

.285

.27

.28

.144

.252

0.266

  1. Average spearman correlations across six different testing sets for all regression and feature selection methods. This data is graphically displayed in Fig. 3