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

Conservative Site-specific Recombination and Phase Variation02:53

Conservative Site-specific Recombination and Phase Variation

6.8K
Because the DNA segments are cut and reorganized in a direction-specific manner, site-specific recombination has emerged as an efficient genetic engineering technique. Flippase and Cyclization recombinases or Flp and Cre, respectively, are two members of the tyrosine recombinase family derived from bacteriophages, that are used to mediate site-specific DNA insertions, deletions, and targeted expression of proteins in mammalian cell lines.
The recognition sites for Cre recombinase called LoxP...
6.8K
Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

1.3K
In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
1.3K
T Cell Types and Functions01:24

T Cell Types and Functions

2.5K
When T cells with CD4 markers are activated, they give rise to two types of effector cells: helper T cells and regulatory T cells. Meanwhile, T cells with CD8 markers differentiate into effector cytotoxic T cells. The differentiation of CD4 T cells into helper T cell subsets, such as Th1, Th2, and Th17 cells, is dependent on the antigen type, antigen-presenting cell, and regulatory cytokines.
Th1 cells stimulate dendritic cells to express necessary co-stimulatory molecules on their surfaces for...
2.5K
Types of Functions I01:26

Types of Functions I

309
Functions are fundamental mathematical tools that capture relationships between variables and describe how one quantity changes in relation to another. Their diverse forms allow them to model various real-world phenomena with precision and flexibility. Among the various categories, algebraic functions are prominent due to their formulation through basic arithmetic operations: addition, subtraction, multiplication, division, and root extraction.Algebraic functions include polynomial, rational,...
309
Types of Functions II01:19

Types of Functions II

195
Trigonometric and exponential functions are essential mathematical tools used to model distinct types of real-world behavior, particularly in periodic and growth-related phenomena. These functions extend the capabilities of basic algebraic models by capturing recurring cycles and rapid changes across various scientific and engineering contexts.Trigonometric functions, such as sine and cosine, are particularly effective for representing periodic phenomena. Their cyclic behavior makes them...
195
Cell Adhesion Molecules - Types and Functions01:20

Cell Adhesion Molecules - Types and Functions

9.3K
Cell adhesion molecules (CAMs) are pivotal to multicellularity and the coordinated functioning of tissues and organ systems. They enable physical interactions between cells and provide mechanical strength to tissues. They also function as receptors for signal transmission across the plasma membrane. The CAMs are broadly classified into four families - integrins, cadherins, selectins, and immunoglobulin-like CAMs (IgCAMs).
CAM Families
The Integrin family of proteins is primarily  involved...
9.3K

You might also read

Related Articles

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

Sort by
Same author

Computationally efficient whole-genome quantile regression at biobank scale.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same author

Liability threshold model-based disease risk prediction based on electronic health record phenotypes.

Nature genetics·2025
Same author

Author Correction: Exome analysis links kidney malformations to developmental disorders and reveals causal genes.

Nature communications·2025
Same author

Exome analysis links kidney malformations to developmental disorders and reveals causal genes.

Nature communications·2025
Same author

Quantile-specific confounding: correction for subtle population stratification via quantile regression.

Genetics·2025
Same author

Uncovering Heterogeneous Effects via Localized Feature Selection.

bioRxiv : the preprint server for biology·2025
Same journal

Large-scale discovery and annotation of substructure patterns in mass spectrometry profiles.

Nature communications·2026
Same journal

Salmonella SopB suppresses post-transcriptionally regulated cytokine release to reduce early tissue inflammation and delay disease progression.

Nature communications·2026
Same journal

A human-specific microRNA controls the timing of excitatory synaptogenesis.

Nature communications·2026
Same journal

An HMA-like integrated domain in the wheat tandem kinase WTK4 recognises an RNase-like pathogen effector.

Nature communications·2026
Same journal

Learning regularities in noise engages both neural predictive activity and representational changes.

Nature communications·2026
Same journal

The H3K4 methyltransferase KMT2D is an essential cofactor for GATA1 at erythroid gene enhancers.

Nature communications·2026
See all related articles

Related Experiment Video

Updated: Feb 1, 2026

A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

69.8K

A semi-supervised approach for predicting cell-type specific functional consequences of non-coding variation using

Zihuai He1, Linxi Liu2, Kai Wang3

  • 1Department of Biostatistics, Columbia University, New York, 10032, NY, USA.

Nature Communications
|December 7, 2018
PubMed
Summary
This summary is machine-generated.

GenoNet, a novel semi-supervised method, accurately predicts the functional impact of genetic variants in non-coding DNA. This approach enhances disease gene discovery and variant interpretation in genomic studies.

More Related Videos

qKAT: Quantitative Semi-automated Typing of Killer-cell Immunoglobulin-like Receptor Genes
07:58

qKAT: Quantitative Semi-automated Typing of Killer-cell Immunoglobulin-like Receptor Genes

Published on: March 6, 2019

9.1K
Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis
09:16

Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis

Published on: June 18, 2020

7.3K

Related Experiment Videos

Last Updated: Feb 1, 2026

A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

69.8K
qKAT: Quantitative Semi-automated Typing of Killer-cell Immunoglobulin-like Receptor Genes
07:58

qKAT: Quantitative Semi-automated Typing of Killer-cell Immunoglobulin-like Receptor Genes

Published on: March 6, 2019

9.1K
Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis
09:16

Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis

Published on: June 18, 2020

7.3K

Area of Science:

  • Genomics
  • Computational Biology
  • Bioinformatics

Background:

  • Predicting the functional impact of genetic variants in non-coding DNA remains a significant challenge in genomics.
  • Non-coding variants constitute a large proportion of human genetic variation, yet their roles in disease are often difficult to ascertain.
  • Existing methods struggle to effectively integrate diverse data types for accurate functional prediction.

Purpose of the Study:

  • To develop a semi-supervised machine learning approach, GenoNet, for predicting the functional consequences of non-coding genetic variants.
  • To leverage labeled regulatory variants, large-scale unlabeled variants, and extensive epigenetic annotations for improved prediction accuracy.
  • To demonstrate the utility of GenoNet in fine-mapping GWAS loci and identifying disease-associated genes.

Main Methods:

  • GenoNet employs a semi-supervised learning framework, integrating experimentally validated regulatory variants (labeled data) with millions of unlabeled variants genome-wide.
  • The method incorporates over a thousand cell/tissue-type specific epigenetic annotations to inform variant function prediction.
  • Model performance was evaluated against existing functional prediction methods using several experimental datasets.

Main Results:

  • GenoNet significantly enhances the prediction accuracy of non-coding variant function at both tissue/cell type and organism levels compared to current methods.
  • The GenoNet scores proved valuable for refining the location of potential causal variants within Genome-Wide Association Study (GWAS) loci.
  • The approach facilitates the discovery of novel disease-associated genes through the analysis of sequencing data.

Conclusions:

  • Semi-supervised learning, as implemented in GenoNet, offers a powerful strategy for predicting non-coding variant function.
  • GenoNet's improved accuracy and applicability to GWAS and sequencing data advance our ability to interpret genomic variation.
  • As validated variant datasets grow, GenoNet and similar methods will become increasingly crucial for comprehensive functional variant prediction across diverse biological contexts.