Gene selection in Cox regression model based on a new adaptive penalized method
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Summary
An adaptive penalized Cox proportional hazards regression model is proposed, with the aim of identification relevant genes and provides high classification accuracy, by combining the Cox proportional hazard regression model with the weighted least absolute shrinkage and selection operator (LASSO) method.
- Type
- article
- Published
- 2020-05-15
- Cited by
- 0
- References
- 37
- Access
- Open access
- OpenAlex
- https://openalex.org/W3083243621
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:221742994
Keywords
Proportional hazards model, Lasso (programming language), Regression, Regression analysis, Selection (genetic algorithm)
References
- The L1/2 regularization network Cox model for analysis of genomic data
- Penalized logistic regression with the adaptive LASSO for gene selection in high-dimensional cancer classification
- Regularized logistic regression with adjusted adaptive elastic net for gene selection in high dimensional cancer classification
- A transcriptome analysis by lasso penalized Cox regression for pancreatic cancer survival.
- PENALIZED VARIABLE SELECTION PROCEDURE FOR COX MODELS WITH SEMIPARAMETRIC RELATIVE RISK
- The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma.
- ORACLE INEQUALITIES FOR THE LASSO IN THE COX MODEL
- Addendum: Regularization and variable selection via the elastic net
- Gene-expression profiles predict survival of patients with lung adenocarcinoma
- The Adaptive Lasso and Its Oracle Properties
- L1 Penalized Estimation in the Cox Proportional Hazards Model
- The L1/2 regularization method for variable selection in the Cox model
- Penalized spline smoothing in multivariable survival models with varying coefficients
- Cross‐validated Cox regression on microarray gene expression data
- The Dantzig Selector for Censored Linear Regression Models
- Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
- Model selection in nonparametric hazard regression
- Penalized Empirical Likelihood via Bridge Estimator in Cox's Proportional Hazard Model
- Survival Prediction Based on Compound Covariate under Cox Proportional Hazard Models
- Regression Shrinkage and Selection via the Lasso
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