The least absolute shrinkage and selection operator regression analysis was used for feature selection.

The least absolute shrinkage and selection operator penalized Cox regression was adopted to construct a radiomic signature.

A radiomic signature was constructed using the least absolute shrinkage and selection operator algorithm in the training set.

The final model was created using a penalized regression method known as the least absolute shrinkage and selection operator.

The least absolute shrinkage and selection operator regression were applied to optimize factor selection for the poor recovery risk model.

Multinomial least absolute shrinkage and selection operator analysis identified 269 genetic variants that showed different frequencies among the three ethnic groups.

Then, we generated a 16-mRNA signature score system through least absolute shrinkage and selection operator (LASSO) Cox regression analysis.

Furthermore, a least absolute shrinkage and selection operator (LASSO) regression analysis was performed to generate TME-related immune prognostic signatures.

The radiomics features were selected with the least absolute shrinkage and selection operator algorithm, and prediction models were constructed with multiple classifiers.

A multivariable least absolute shrinkage and selection operator regression analysis was used to adjust for patient pre- and intraoperative risk factors for mortality.

For these genes, least absolute shrinkage and selection operator (lasso) regression model was applied and validated to build a diagnostic risk score model.

Multiple regression analysis was performed using generalized linear models (GLMs) and the least absolute shrinkage and selection operator (LASSO) approach for variable selection.

The statistical method of LASSO ( least absolute shrinkage and selection operator) was used to select important placental measures that would have better predictability for outcomes.

A novel preconditioned least absolute shrinkage and selection operator method yielded an average r s of 0.38 on 100 bootstrapped data sets.

A logistic least absolute shrinkage and selection operator (LASSO) regression with 10-fold cross-validation was used to select potential predictors among 47 candidate variables.