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Table 1 Tissue-Specific Test

From: Improving prediction of phenotypic drug response on cancer cell lines using deep convolutional network

Tissue name

Data amount

R2

Rp

RMSE

Aero digestive

13806

0.703

0.843

0.0375

Tract

 

(0.826)

(0.916)

(0.0280)

Blood

31119

0.500

0.724

0.0449

  

(0.833)

(0.917)

(0.0276)

Bone

6826

0.659

0.813

0.0405

  

(0.825)

(0.915)

(0.0283)

Breast

9277

0.657

0.811

0.0383

  

(0.829)

(0.919)

(0.0281)

Digestive

17200

0.667

0.817

0.0384

System

 

(0.830)

(0.918)

(0.0282)

Kidney

5199

0.669

0.819

0.0386

  

(0.822)

(0.914)

(0.0286)

Lung

34086

0.614

0.784

0.0371

  

(0.827)

(0.919)

(0.0285)

Nervous

15763

0.702

0.839

0.0364

System

 

(0.830)

(0.918)

(0.0280)

Pancreas

5358

0.703

0.840

0.0370

  

(0.820)

(0.913)

(0.0287)

Skin

10488

0.676

0.824

0.0394

  

(0.827)

(0.917)

(0.0281)

Soft tissue

3165

0.712

0.853

0.0384

  

(0.821)

(0.914)

(0.0284)

Thyroid

2715

0.672

0.822

0.0410

  

(0.833)

(0.918)

(0.0277)

Urogenital

17112

0.715

0.849

0.0363

System

 

(0.825)

(0.914)

(0.0282)

  1. The first column is the 13 tissue names which are ranged in alphabetical order. The second column is the number of the ground true IC50 values for each tissue. The last three columns are R2, Rp and RMSE that our model tCNNS achieved by training on all the other tissue data. The number in the bracket is the result for the validation set