Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Multiple Regression01:25

Multiple Regression

4.2K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
4.2K
Regression Toward the Mean01:52

Regression Toward the Mean

7.2K
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...
7.2K
Regression Analysis01:11

Regression Analysis

8.7K
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:
8.7K
Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

4.7K
A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
4.7K
Frequency-dependent Selection01:21

Frequency-dependent Selection

24.3K
When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.
24.3K
Combinatorial Gene Control02:33

Combinatorial Gene Control

9.8K
Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
The expression of more than 30,000 genes is controlled by approximately 2000-3000 transcription factors. This is possible because a single transcription factor can recognize more than one regulatory sequence. The specificity in gene...
9.8K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Comparison of pathway and gene-level models for cancer prognosis prediction.

BMC bioinformatics·2020
Same author

Novel Genetic Variants of <i>ALG6</i> and <i>GALNTL4</i> of the Glycosylation Pathway Predict Cutaneous Melanoma-Specific Survival.

Cancers·2020
Same author

Immune-mediated genetic pathways resulting in pulmonary function impairment increase lung cancer susceptibility.

Nature communications·2020
Same author

Whole Exome Sequencing of Highly Aggregated Lung Cancer Families Reveals Linked Loci for Increased Cancer Risk on Chromosomes 12q, 7p, and 4q.

Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology·2019
Same author

Clinical relevance of TP53 hotspot mutations in high-grade serous ovarian cancers.

British journal of cancer·2019
Same author

Genome-Wide Profiling of Acquired Uniparental Disomy Reveals Prognostic Factors in Head and Neck Squamous Cell Carcinoma.

Neoplasia (New York, N.Y.)·2019

Related Experiment Video

Updated: Mar 3, 2026

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
09:35

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Published on: August 16, 2017

18.4K

Gene set selection via LASSO penalized regression (SLPR).

H Robert Frost1, Christopher I Amos1

  • 1Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Hanover, NH 03755, USA.

Nucleic Acids Research
|May 5, 2017
PubMed
Summary

Gene set selection via LASSO Penalized Regression (SLPR) improves bioinformatics analysis by using continuous gene activity measures. This novel method outperforms existing techniques for gene set testing in large collections.

More Related Videos

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

1.4K
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

8.1K

Related Experiment Videos

Last Updated: Mar 3, 2026

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
09:35

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Published on: August 16, 2017

18.4K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

1.4K
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

8.1K

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Gene set testing is crucial for interpreting complex biological data and improving research reproducibility.
  • Existing multiset methods for analyzing large gene set collections often rely on simplified binary gene activity, limiting their performance.
  • These methods struggle with the nuances of genomic data where gene activity is not strictly binary.

Purpose of the Study:

  • To develop an advanced multiset gene set testing method that overcomes the limitations of binary activity indicators.
  • To introduce a novel approach that leverages continuous measures of gene activity for more accurate gene set evaluation.
  • To enhance the analysis of large and overlapping gene set collections in bioinformatics.

Main Methods:

  • Developed gene set Selection via LASSO Penalized Regression (SLPR), mapping multiset testing to penalized multiple linear regression.
  • SLPR models a linear relationship between continuous gene activity measures and the activity of all gene sets.
  • Utilized simulation studies and analysis of TCGA data with MSigDB gene sets to validate the method.

Main Results:

  • SLPR demonstrated superior performance compared to existing multiset methods.
  • The method excels when biological processes are well-represented by continuous activity measures and linear gene-set associations.
  • Analysis of TCGA data confirmed the effectiveness of SLPR in identifying relevant gene sets.

Conclusions:

  • SLPR offers a more powerful and flexible approach to multiset gene set testing.
  • The method provides improved performance by utilizing continuous gene activity data.
  • SLPR represents a significant advancement for analyzing complex genomic datasets and large gene set collections.