selection operator lasso regression  (Genovis Inc)

Journal: Journal of Cardiothoracic Surgery

Article Title: Screening telomere-related genes to predict prognosis, immunotherapy response, and drug sensitivity in esophageal cancer using a machine learning approach

doi: 10.1186/s13019-025-03727-w

Figure Lengend Snippet: Machine learning method was used to screen TRDGs. A : Forest plot showed the results of univariate COX regression analysis; B and C : Lasso regression analysis showed that the curve was the lowest when lambda = 0.07, and 8 genes were finally obtained. D : The bar graph shows the RF analysis results, the genes were ranked according to the Gini coefficient for importance, and genes with Gini > 2 were selected for subsequent analysis. E and F : SVM analysis showed that the maximum accuracy and the lowest error rate could be achieved when the number of genes was 22

Article Snippet: Prognostic TRGs were identified using multivariate Cox regression analysis, Least Absolute Shrinkage and Selection Operator (LASSO), Random Forest (RF), and Support Vector Machine (SVM) algorithms to construct a risk model. Model performance was evaluated by Kaplan–Meier(K-M) and Receiver Operating Characteristic (ROC) analyses, and a nomogram integrating clinical variables was developed.

Techniques:

Journal: Journal of Cachexia, Sarcopenia and Muscle

Article Title: From Symptom to Outcome: Defining Clinically Meaningful Patient‐Reported Appetite Loss in Non‐Small‐Cell Lung Cancer

doi: 10.1002/jcsm.70150

Figure Lengend Snippet: Associations between appetite–body weight model parameters and overall survival. Cox proportional hazards model incorporating model parameters and LASSO‐selected survival‐related clinical characteristics for recovering study cohort (A) or chemotherapy study cohort (B). The black boxes with horizontal error bars represent hazard ratio estimates with 95% CI. p values for each covariate are labelled on the right. LASSO, least absolute shrinkage and selection operator; CI, confidence interval; ECOG, Eastern Cooperative Oncology Group. Significance: *, p < 0.05; **, p < 0.01; ***, p < 0.001.

Article Snippet: Prognostic clinical variables were identified using the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm, with fivefold cross‐validation.

Techniques: Selection