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

Cis-regulatory Sequences02:02

Cis-regulatory Sequences

11.8K
Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...
11.8K
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

490
Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
490
Application of Integration: Problem Solving01:30

Application of Integration: Problem Solving

99
The process of breathing involves the periodic intake and expulsion of air, known as the respiratory cycle, which typically lasts about five seconds. Modeling the volume of air inhaled into the lungs as a function of time provides insight into both the dynamics and efficiency of pulmonary ventilation. This volume is determined by integrating the airflow rate over time, which captures the cumulative effect of air entering the lungs.Sinusoidal Model of AirflowAirflow during respiration is not...
99
Applications of Integration to Find Hydrostatic Pressure01:30

Applications of Integration to Find Hydrostatic Pressure

86
Hydrostatic force is a fluid's total force at rest on a surface. For a horizontal surface submerged at a fixed depth, the pressure is constant and calculated as the product of fluid density, gravitational acceleration, and depth. In the case of a vertical dam wall submerged in water, this force is not evenly distributed due to the increasing pressure with depth. This variation arises from the cumulative weight of the water above each point. Integration is used to account for the continuous...
86
Applications of Integration to Find Centers of Mass01:30

Applications of Integration to Find Centers of Mass

80
Rotational equilibrium provides a natural framework for defining the center of mass of a system. For a plank balanced on a pivot with two unequal masses, equilibrium is achieved when the net torque about the pivot is zero. Torque is defined as the product of a force and its perpendicular distance from the pivot. When the torques due to all forces cancel, the pivot coincides with the center of mass of the system.For a system composed of several discrete point masses, the center of mass lies at...
80
Applications of Integration to Find Blood Flow01:27

Applications of Integration to Find Blood Flow

51
Blood flow through a cylindrical blood vessel can be mathematically described using the principles of laminar flow, a regime in which fluid moves smoothly in parallel layers. In this model, the velocity of the blood is not uniform across the cross-section of the vessel; rather, it varies with the radial distance from the center. The maximum velocity occurs along the central axis, decreasing progressively toward the vessel walls, where it reaches zero due to viscous drag.Approximating Blood...
51

You might also read

Related Articles

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

Sort by
Same author

Human-Structure-Aware Token Position Embedding for Tokenized Pose Estimation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

MetaCancerDB: a database of site-specific RNA-miRNA correlations in cancer metastasis.

Database : the journal of biological databases and curation·2026
Same author

GraphLooper: predicting chromatin loops based on hierarchical multi-view graph pooling method.

Briefings in bioinformatics·2026
Same author

A functional investigation of antibody Fc-FcRn variant binding guided by <i>in silico</i> free energy perturbation methods.

bioRxiv : the preprint server for biology·2026
Same author

Proteome-wide prediction of interactions between structured domains and peptide motifs reveals functionally coherent subnetworks.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

LMSCDA: A Secondary Structure Enhanced Language Model for Predicting CircRNA and Disease Associations.

IEEE journal of biomedical and health informatics·2026

Related Experiment Video

Updated: Feb 6, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.7K

Integration of Multi-Omics Data for Gene Regulatory Network Inference and Application to Breast Cancer.

Lin Yuan, Le-Hang Guo, Chang-An Yuan

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |August 24, 2018
    PubMed
    Summary

    This study introduces BMNPGRN, a novel machine learning method for inferring gene regulatory networks. It effectively integrates multi-omics data to identify cancer-related gene networks, outperforming existing approaches.

    More Related Videos

    Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
    07:41

    Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

    Published on: May 17, 2019

    9.6K
    Integrating Augmented Reality Tools in Breast Cancer Related Lymphedema Prognostication and Diagnosis
    06:03

    Integrating Augmented Reality Tools in Breast Cancer Related Lymphedema Prognostication and Diagnosis

    Published on: February 6, 2020

    7.2K

    Related Experiment Videos

    Last Updated: Feb 6, 2026

    Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
    10:44

    Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

    Published on: December 7, 2021

    2.7K
    Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
    07:41

    Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

    Published on: May 17, 2019

    9.6K
    Integrating Augmented Reality Tools in Breast Cancer Related Lymphedema Prognostication and Diagnosis
    06:03

    Integrating Augmented Reality Tools in Breast Cancer Related Lymphedema Prognostication and Diagnosis

    Published on: February 6, 2020

    7.2K

    Area of Science:

    • Computational Biology
    • Genomics
    • Bioinformatics

    Background:

    • Cancer development is driven by complex gene regulatory networks.
    • Inferring these networks is challenging due to limited sample sizes and multifactorial influences.
    • High-throughput technologies and multi-omics data offer new avenues for network inference.

    Purpose of the Study:

    • To develop a machine learning approach for robust gene regulatory network inference.
    • To incorporate multi-omics data, including DNA methylation and copy number variation, into network modeling.
    • To enhance the accuracy of cancer-related gene regulatory network identification.

    Main Methods:

    • Proposed a novel method, biweight midcorrelation and nonconvex penalty based sparse regression for gene regulatory network inference (BMNPGRN).
    • Integrated multi-omics data (DNA methylation, copy number variation) and their interactions.
    • Utilized synthetic datasets for performance evaluation against established methods.

    Main Results:

    • BMNPGRN demonstrated superior performance compared to DCGRN, ARACNE, and CLR under false positive control on synthetic data.
    • The method successfully identified a gene regulatory network in breast cancer (BRCA) data.
    • BMNPGRN effectively captures complex regulatory relationships using integrated multi-omics information.

    Conclusions:

    • BMNPGRN provides a powerful and accurate approach for gene regulatory network inference.
    • The integration of multi-omics data significantly improves the identification of cancer-related networks.
    • This method offers a valuable tool for understanding cancer biology and developing targeted therapies.