Table 3 Comparison of predictive performance for AUC from three different approaches: RF, FRFL and FRF using CCLE data.

From: Functional random forest with applications in dose-response predictions

Drug

Correlation

MAE

 

KL divergence

Hellinger Distance

 

KL divergence

Hellinger Distance

RF

FRFL

FRF

FRFL

FRF

RF

FRFL

FRF

FRFL

FRF

Erlotinib

0.4408

0.4473

0.4620

0.4265

0.4643

0.0546

0.0544

0.0466

0.0552

0.0472

Nilotinib

0.3886

0.4263

0.4601

0.4475

0.5009

0.0465

0.0459

0.0375

0.0457

0.0373

PD-0325901

0.4716

0.5149

0.5775

0.4920

0.5633

0.1353

0.1330

0.1370

0.1352

0.1386

PLX-4720

0.2957

0.3168

0.4308

0.3314

0.4491

0.0494

0.0489

0.0398

0.0492

0.0397

TAE-684

0.2757

0.3245

0.3689

0.2860

0.3337

0.0728

0.0723

0.0688

0.0730

0.0697

  1. For FRFL and FRF, node cost is calculated using f-divergences (KL divergence or Hellinger distance) of the response distributions at 8 different doses. Bold values indicate the best performances.
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