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Related Experiment Video

Updated: Jun 12, 2026

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
03:08

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Published on: October 3, 2025

Development and validation of predictive indices for a continuous outcome using gene expression profiles.

Yingdong Zhao1, Richard Simon

  • 1Biometric Research Branch, Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA.

Cancer Informatics
|June 5, 2010
PubMed
Summary

This study evaluates linear regression for high-dimensional gene expression data, finding LASSO and LAR methods effective for predicting continuous responses, outperforming ALM.

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The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the addition of a...

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Area of Science:

  • Bioinformatics
  • Statistical Genomics
  • Computational Biology

Background:

  • Linear regression is challenging for high-dimensional gene expression data where predictors exceed samples.
  • Few studies address prediction of continuous responses using microarray data with standard linear models.

Purpose of the Study:

  • To evaluate three linear regression algorithms for predicting continuous responses from high-dimensional gene expression data.
  • To compare the performance of Least Angle Regression (LAR), Least Absolute Shrinkage and Selection Operator (LASSO), and Averaged Linear Regression Method (ALM).

Main Methods:

  • Simulations using a real gene expression dataset.
  • Analyses of two real gene expression datasets.
  • Unbiased complete cross-validation approach for model evaluation.
Keywords:
continuous outcomegene expressionregression model

Related Experiment Videos

Last Updated: Jun 12, 2026

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
03:08

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Published on: October 3, 2025

Main Results:

  • LASSO often yields models with lower prediction error compared to LAR.
  • Both LASSO and LAR demonstrate more efficient performance than ALM.
  • The developed plug-in for BRB-ArrayTools implements LAR and LASSO with complete cross-validation.

Conclusions:

  • LASSO and LAR are effective methods for predicting continuous responses from high-dimensional gene expression data.
  • These algorithms offer improved prediction accuracy and efficiency over ALM.
  • The BRB-ArrayTools plug-in provides a practical implementation for researchers using these advanced methods.