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Related Concept Videos

Prediction Intervals01:03

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
The...
Multiple Regression01:25

Multiple Regression

Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
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Regression Analysis01:11

Regression Analysis

Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
Regression Toward the Mean01:52

Regression Toward the Mean

Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when researchers try to extrapolate results...
DNA Microarrays02:34

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
Survival Tree01:19

Survival Tree

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 Building a Survival Tree
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Related Experiment Video

Updated: Jun 21, 2026

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets
03:37

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Published on: March 1, 2024

Incorporating predictor network in penalized regression with application to microarray data.

Wei Pan1, Benhuai Xie, Xiaotong Shen

  • 1Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota 55455, USA. weip@biostat.umn.edu

Biometrics
|August 4, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a novel network-based penalized regression method for "large p, small n" problems. The approach enhances grouped variable selection and outperforms existing methods in simulations and glioblastoma patient survival prediction.

Related Experiment Videos

Last Updated: Jun 21, 2026

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets
03:37

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Published on: March 1, 2024

Area of Science:

  • Statistics
  • Bioinformatics
  • Genomics

Background:

  • Penalized linear regression is crucial for high-dimensional data ('large p, small n').
  • Prior network information, like gene pathways, can improve predictive modeling.
  • Existing methods may not fully leverage network structures for variable selection.

Purpose of the Study:

  • To develop a penalized regression method incorporating prior network information.
  • To enable grouped variable selection by smoothing coefficients over a network.
  • To improve prediction accuracy and variable selection in 'large p, small n' settings.

Main Methods:

  • A novel grouped penalty based on the L(gamma)-norm is proposed.
  • The penalty smooths regression coefficients of connected predictors in a network.
  • The method is evaluated using simulation studies and a glioblastoma microarray dataset.

Main Results:

  • The proposed method demonstrates superior finite-sample performance compared to Lasso, elastic net, and other network-based methods.
  • It excels in grouped variable selection across various simulation scenarios.
  • The method successfully predicts glioblastoma patient survival using gene expression and network data.

Conclusions:

  • The proposed network-based penalized regression method effectively utilizes prior network information for variable selection.
  • It offers an advantage over existing methods, particularly in 'large p, small n' scenarios.
  • The approach has practical applications in analyzing complex biological data, such as gene expression.