Robust adaptive Lasso for variable selection

Construction of an anthropometric discriminant model for identification of elite swimmers: an adaptive lasso approach

Background

Anthropometric characteristics are important factors that affect swimming performance. The aim of this study is to build a discriminant model using anthropometric factors to identify elite short-to-medium-distance freestyle swimmers through an adaptive Lasso approach.

Methods

The study recruited 254 swimmers (145 males and 109 females) who were divided them into elite (aged 17.9 ± 2.2 years, FINA points 793.8 ± 73.8) and non-elite (aged 17.1 ± 1.3 years, FINA points 560.6 ± 78.7) groups. Data for 73 variables were obtained, including basic information, anthropometric and derivative indicators. After filtering out highly correlated variables, 24 candidate variables were retained to be used in adaptive Lasso to select variables for prediction of elite swimmers. Deviance and area under the curve (AUC) were applied to assess the goodness of fit and prediction accuracy of the model, respectively.

Results

The adaptive Lasso selected 12 variables using the whole sample, with an AUC being 0.926 (95% CI [0.895-0.956]; P = 2.42 × 10^-29). In stratified analysis by gender, nine variables were selected for male swimmers with an AUC of 0.921 (95% CI [0.880-0.963]; P = 8.82 × 10^-17), and eight variables were for female swimmers with an AUC of 0.941 (95% CI [0.898-0.984]; P = 7.67 × 10^-15).

Conclusion

The adaptive Lasso showed satisfactory performance in selecting anthropometric characteristics to identify elite swimmers. Additional studies with longitudinal data or data from other ethnicities are needed to validate our findings.

Logistic regression with adaptive sparse group lasso penalty and its application in acute leukemia diagnosis

Cancer diagnosis based on gene expression profile data has attracted extensive attention in computational biology and medicine. It suffers from three challenges in practical applications: noise, gene grouping, and adaptive gene selection. This paper aims to solve the above problems by developing the logistic regression with adaptive sparse group lasso penalty (LR-ASGL). A noise information processing method for cancer gene expression profile data is first presented via robust principal component analysis. Genes are then divided into groups by performing weighted gene co-expression network analysis on the clean matrix. By approximating the relative value of the noise size, gene reliability criterion and robust evaluation criterion are proposed. Finally, LR-ASGL is presented for simultaneous cancer diagnosis and adaptive gene selection. The performance of the proposed method is compared with the other four methods in three simulation settings: Gaussian noise, uniformly distributed noise, and mixed noise. The acute leukemia data are adopted as an experimental example to demonstrate the advantages of LR-ASGL in prediction and gene selection.

RAMRSGL: A Robust Adaptive Multinomial Regression Model for Multicancer Classification

In view of the challenges of the group Lasso penalty methods for multicancer microarray data analysis, e.g., dividing genes into groups in advance and biological interpretability, we propose a robust adaptive multinomial regression with sparse group Lasso penalty (RAMRSGL) model. By adopting the overlapping clustering strategy, affinity propagation clustering is employed to obtain each cancer gene subtype, which explores the group structure of each cancer subtype and merges the groups of all subtypes. In addition, the data-driven weights based on noise are added to the sparse group Lasso penalty, combining with the multinomial log-likelihood function to perform multiclassification and adaptive group gene selection simultaneously. The experimental results on acute leukemia data verify the effectiveness of the proposed method.