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

Classification of Systems-I01:26

Classification of Systems-I

169
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
169
Causality in Epidemiology01:21

Causality in Epidemiology

321
Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
321
Classification of Systems-II01:31

Classification of Systems-II

134
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
134
Signal and System01:26

Signal and System

625
A signal x(t) is a set of data or a time function representing a variable of interest. Signals typically convey information about a phenomenon, such as atmospheric temperature, humidity, human voice, television images, a dog's bark, or birdsongs. More generally, a signal can be a function of more than one independent variable. For instance, images depend on horizontal and vertical positions and can be regarded as two-dimensional signals. However, this text will focus on one-dimensional...
625
Correlation and Causation01:27

Correlation and Causation

37.5K
Statistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. An indirect relationship of the variables signifies a correlation, while a direct relationship shows causation. If it is determined that no connection exists between the variables, then the correlation is a coincidence.
Correlation versus Causation
If the dependent variable increases or decreases when the independent variable increases, there is a positive or negative...
37.5K
State Space Representation01:27

State Space Representation

165
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
165

You might also read

Related Articles

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

Sort by
Same author

The roles of neuroticism, genetic susceptibility, and amygdala structure in chronic musculoskeletal pain onset and recovery.

Communications medicine·2026
Same author

DataAtlas: automatic generation of data dictionaries using large language models.

JAMIA open·2026
Same author

Genomic characterisation of the outbreak-associated hantavirus strain.

Infectious diseases (London, England)·2026
Same author

Integrated Downstream Analysis and Epidemiological Modelling of Hantavirus Infection: From Host Transcriptomics to Transmission Dynamics.

Pathogens (Basel, Switzerland)·2026
Same author

Integrated Evolutionary and Multi-Omic Analysis of STAT Family Activation Across Solid Tumors.

Genes·2026
Same author

An Innovative 3D Slicer Plugin for Brain Images Annotation and Lesions Study.

Studies in health technology and informatics·2026

Related Experiment Video

Updated: Jun 7, 2025

Perspectives on Neuroscience
00:26

Perspectives on Neuroscience

Published on: July 31, 2007

4.9K

Understanding complex systems through differential causal networks.

Annamaria Defilippo1, Federico Manuel Giorgi2, Pierangelo Veltri3

  • 1Department of Surgical and Medical Sciences, Magna Graecia University of Catanzaro, Catanzaro, 88100, Italy.

Scientific Reports
|November 9, 2024
PubMed
Summary
This summary is machine-generated.

Differential Causal Networks (DCNs) reveal sex-specific gene differences in type 2 diabetes. This new framework models causal relationships, offering insights into biological information flow and interventions.

More Related Videos

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

2.1K
Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
08:43

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Published on: August 7, 2017

7.9K

Related Experiment Videos

Last Updated: Jun 7, 2025

Perspectives on Neuroscience
00:26

Perspectives on Neuroscience

Published on: July 31, 2007

4.9K
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

2.1K
Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
08:43

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Published on: August 7, 2017

7.9K

Area of Science:

  • Computational biology
  • Data science
  • Systems biology

Background:

  • Causal Networks (CNs) model relationships in complex systems from data.
  • Comparing systems reveals rewiring in different cells, tissues, and states.
  • Existing differential networks lack causal direction, limiting biological interpretation.

Purpose of the Study:

  • Introduce Differential Causal Networks (DCNs) to model differences between CNs.
  • Provide a robust framework for analyzing causal relationship differences.
  • Enable better understanding of information flow and interventions.

Main Methods:

  • Developed a novel framework, Differential Causal Networks (DCNs).
  • Obtained DCNs by comparing two existing CNs derived from experimental data.
  • Tested DCNs on gene expression data related to type 2 diabetes, considering sex and tissue.

Main Results:

  • DCNs successfully highlighted causal differences between sexes across nine tissues.
  • Compared three DCN definitions, revealing biologically significant similarities and differences.
  • The framework provides a powerful tool for identifying differential causal relations.

Conclusions:

  • DCNs offer a robust method for comparing causal structures between biological systems.
  • The approach enhances understanding of sex-specific molecular mechanisms in type 2 diabetes.
  • This framework facilitates deeper insights into biological information flow and potential interventions.