Robust regularization for high-dimensional Cox’s regression model using weighted likelihood criterion

Wahid, Abdul; Khan, Dost Muhammad; Khan, Sajjad Ahmad; Hussain, Ijaz; Khan, Zardad

doi:10.1016/j.chemolab.2021.104285

Cited by 5 publications

(3 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To avoid overfitting, regularisation techniques such as ridge regression [190] or LASSO (Least Absolute Shrinkage and Selection Operator) [191] , [192] can be adopted during the model training phase. In Cox analysis regularization can help in reducing the weight of less relevant or redundant variables, limiting the complexity of the model and keeping only most influential variables [193] . For example, LASSO is effective in excluding insignificant variables by selecting an optimal subset of predictors [191] , [192] .…”

Section: Limitationsmentioning

confidence: 99%

Differential gene expression analysis pipelines and bioinformatic tools for the identification of specific biomarkers: A review

Rosati,

Palmieri,

Brunelli

et al. 2024

Computational and Structural Biotechnology Journal

View full text Add to dashboard Cite

Section: Limitationsmentioning

confidence: 99%

Differential gene expression analysis pipelines and bioinformatic tools for the identification of specific biomarkers: A review

Rosati,

Palmieri,

Brunelli

et al. 2024

Computational and Structural Biotechnology Journal

View full text Add to dashboard Cite

“…For example, human microarray data contains a large amount of genetic information. However, only a few genes are associated with Leukemia, and these genes associated with specific diseases are also called biomarkers [1]. Understanding how to obtain disease-specific biomarkers from microarray data is important for clinical diagnosis, drug development and disease understanding.…”

Section: Introductionmentioning

confidence: 99%

A Novel Feature Selection Method Based on Binary Differential Evolution and Feature Subset Correlation for Microarray Data

Xie

Chi

Wang

et al. 2022

Preprint

View full text Add to dashboard Cite

Obtaining diagnostically important features from microarray data can help researchers understand diseases and study drugs. The challenges come from the high dimensionality, small sample size of the data and the high redundancy among features. To solve this problem, we propose a new hybrid feature selection method based on differential evolution algorithm. The proposed method the proposed method adopts a new binary quantization method to adapt to the characteristics of high-dimensional data, and improves the scaling factor and mutation operator to balance the global search performance and convergence speed of the algorithm. The proposed method also proposes the concept of feature subset correlation in the fitness function to ensure the low redundancy of feature subsets. The experimental results show that the proposed method is advanced and the selected features, are with low redundancy and can effectively distinguish between positive and negative samples.

show abstract

“…36 Nevertheless, the statistical literature concerning robust variable selection and parameter estimation for the PH model is very limited. Recently, Wahid et al 37 propose a weighted partial likelihood estimator with the adaptive LASSO to obtain robust sparsity and estimation. Although the method is robust to high leverage points in predictors, it is sensitive to outliers in the response and does not detect outliers.…”

Section: Introductionmentioning

confidence: 99%

Penalized weighted proportional hazards model for robust variable selection and outlier detection

Luo

Gao

Halabi

2022

Statistics in Medicine

View full text Add to dashboard Cite

Identifying exceptional responders or nonresponders is an area of increased research interest in precision medicine as these patients may have different biological or molecular features and therefore may respond differently to therapies.Our motivation stems from a real example from a clinical trial where we are interested in characterizing exceptional prostate cancer responders. We investigate the outlier detection and robust regression problem in the sparse proportional hazards model for censored survival outcomes. The main idea is to model the irregularity of each observation by assigning an individual weight to the hazard function. By applying a LASSO-type penalty on both the model parameters and the log transformation of the weight vector, our proposed method is able to perform variable selection and outlier detection simultaneously. The optimization problem can be transformed to a typical penalized maximum partial likelihood problem and thus it is easy to implement. We further extend the proposed method to deal with the potential outlier masking problem caused by censored outcomes. The performance of the proposed estimator is demonstrated with extensive simulation studies and real data analyses in low-dimensional and high-dimensional settings.

show abstract

Robust regularization for high-dimensional Cox’s regression model using weighted likelihood criterion

Cited by 5 publications

References 34 publications

Differential gene expression analysis pipelines and bioinformatic tools for the identification of specific biomarkers: A review

Differential gene expression analysis pipelines and bioinformatic tools for the identification of specific biomarkers: A review

A Novel Feature Selection Method Based on Binary Differential Evolution and Feature Subset Correlation for Microarray Data

Penalized weighted proportional hazards model for robust variable selection and outlier detection

Contact Info

Product

Resources

About