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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