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Related Experiment Video

Updated: Jan 17, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Stochastic LASSO for extremely high-dimensional genomic data.

Beomsu Baek¹, Jongkwon Jo^2,3, Mingon Kang⁴

¹Department of Computer Science, University of Nevada, Las Vegas, 89154, NV, USA.

Scientific Reports

|January 14, 2026

Summary

This summary is machine-generated.

Stochastic LASSO enhances feature selection for high-dimensional genomic data by reducing multicollinearity and sampling randomness. This new method outperforms existing models in identifying significant biomarkers and estimating coefficients.

Keywords:

High-dimensional data LASSO Stochastic LASSO Variable selection

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Published on: June 23, 2012

Area of Science:

Genomics
Biostatistics
Machine Learning

Background:

High-dimensional data analysis is crucial for genomic studies.
Least Absolute Shrinkage and Selection Operator (LASSO) and its variants are used for biomarker discovery.
Existing bootstrap-based LASSO models face challenges like multicollinearity and sampling randomness.

Purpose of the Study:

To introduce Stochastic LASSO, a novel bootstrap-based method for feature selection.
To address limitations of existing LASSO models in extremely high-dimensional but low sample size (EHDLSS) data.
To improve feature selection accuracy and coefficient estimation in genomic analysis.

Main Methods:

Developed Stochastic LASSO, a new bootstrap-based approach.
Implemented techniques to reduce multicollinearity and sampling randomness.
Utilized a two-stage t-test strategy for statistical significance.
Evaluated performance through extensive simulations and TCGA cancer gene expression data.

Main Results:

Stochastic LASSO demonstrated superior performance in feature selection and coefficient estimation compared to benchmark models.
The method effectively reduced multicollinearity and sampling randomness.
Identified statistically significant genes associated with survival prediction in TCGA cancer datasets.
Showed improved robustness in simulation experiments.

Conclusions:

Stochastic LASSO offers a significant advancement over existing LASSO models for EHDLSS genomic data.
The proposed method provides a robust and accurate tool for biomarker discovery and feature selection.
Stochastic LASSO has practical applications in cancer genomics for survival prediction.