Predicting Absenteeism at Workplace Using Machine Learning and Network Analysis by Donggeun Kim, Jai Woo Lee

“…The proposed method is validated on publicly available data sets using random forest and penalized regression with k-fold cross validation in order to strengthen better generalizability. …”
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Comparing statistical learning methods for complex trait prediction from gene expression. by Noah Klimkowski Arango, Fabio Morgante

“…Genotypes have been used for trait prediction using a variety of methods such as mixed models, Bayesian methods, penalized regression methods, dimension reduction methods, and machine learning methods. …”
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Prognostic and diagnostic value of PVR gene and protein levels, serum amylase, and urinary IGFBP-7 and TIMP-2 biomarkers in multiple myeloma by Eman M. Habib, Asmaa M. Hasan, Sara A. A. Mohammed, Amira A. A. Othman, Rasha Elgamal, Dalia E. Sherief

“…Multivariate Cox regression confirmed PVR expression (HR=12.2), serum amylase (HR=11.5), and IGFBP-7 (HR=11.9) as independent predictors of poor OS, findings that remained robust in bootstrapped and penalized regression models. Stratified analysis revealed that patients in the highest biomarker quartiles had significantly worse outcomes and higher TP53 mutation rates. …”
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Improving risk stratification of PI-RADS 3 + 1 lesions of the peripheral zone: expert lexicon of terms, multi-reader performance and contribution of artificial intelligence by Philip A. Glemser, Nils Netzer, Christian H. Ziener, Markus Wilhelm, Thomas Hielscher, Kevin Sun Zhang, Magdalena Görtz, Viktoria Schütz, Albrecht Stenzinger, Markus Hohenfellner, Heinz-Peter Schlemmer, David Bonekamp

“…These remained after lasso penalized regression based feature reduction, while the only selected clinical feature was prostate volume (OR<1), enabling nomogram construction. …”
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