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Table 3 Prediction results for ordinary lasso sparse modeling and ridge regression as baseline results under the several situations shown in a part of Fig. 1(b)

From: Computational prediction of plasma protein binding of cyclic peptides from small molecule experimental data using sparse modeling techniques

Method

Training set

Test set

RMSE (fb)

MAE (fb)

R (ln Ka)

ridge-CP-LOO

(baseline #3)

Cyclic peptide drugs (LOOCV)

Cyclic peptide drugs (LOOCV)

0.338

0.244

0.418

lasso-CP-LOO

(baseline #4)

Cyclic peptide drugs (LOOCV)

Cyclic peptide drugs (LOOCV)

0.358

0.286

0.289

ridge-CP

(baseline #3)

Cyclic peptide drugs (# = 24)

Synthetic cyclic peptides (# = 16)

0.413

0.354

0.442

lasso-CP

(baseline #4)

Cyclic peptide drugs (# = 24)

Synthetic cyclic peptides (# = 16)

0.688

0.627

0.069