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

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
The Two-State Receptor Model01:29

The Two-State Receptor Model

The two-state receptor model explains a drug's interaction with receptors, such as G protein-coupled receptors and ligand-gated ion channels, to induce or inhibit a biological response. When no natural ligands are present, a receptor exists in an equilibrium of inactive (Ri) and active (Ra) conformations. The inactive form does not produce a response, while the active form generates a basal effect known as constitutive activity.
The binding affinity of a drug determines its interaction with one...
BIBO stability of continuous and discrete -time systems01:24

BIBO stability of continuous and discrete -time systems

System stability is a fundamental concept in signal processing, often assessed using convolution. For a system to be considered bounded-input bounded-output (BIBO) stable, any bounded input signal must produce a bounded output signal. A bounded input signal is one where the modulus does not exceed a certain constant at any point in time.
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system.
Woodward–Hoffmann Selection Rules and Microscopic Reversibility01:34

Woodward–Hoffmann Selection Rules and Microscopic Reversibility

Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...
Biostatistics: Overview01:20

Biostatistics: Overview

Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...

You might also read

Related Articles

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

Sort by
Same author

Long-term prognostic impact of optical coherence tomography detected plaque rupture in nonculprit coronary segments.

Atherosclerosis·2026
Same author

Assessing Ecological Connectivity for <i>Loxodonta africana</i> Across Transfrontier Conservation Areas in Southern Mozambique.

Ecology and evolution·2026
Same author

Duplex ultrasound-based step-by-step perforator mapping by a microsurgeon for DIEP flap planning: A prospective case series.

Journal of plastic, reconstructive & aesthetic surgery : JPRAS·2026
Same author

Prehabilitation clinic in cardiac surgery: multidisciplinary protocol for the improvement of postoperative outcomes.

Journal of cardiothoracic surgery·2026
Same author

Long-Term Breast Morphological Analysis After Ergonomic FALD Flap Reconstruction: A Case-Control Study.

Journal of reconstructive microsurgery·2026
Same author

Frailty elements, direct oral anticoagulants and mortality risk in atrial fibrillation patients aged ≥ 80 or ≥ 90 years from the nationwide Italian START registry: a propensity score matching analysis.

GeroScience·2025

Related Experiment Video

Updated: May 20, 2026

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

A Bayesian autoregressive three-state hidden Markov model for identifying switching monotonic regimes in microarray

Alessio Farcomeni1, Serena Arima

  • 1Sapienza University of Rome, Italy.

Statistical Applications in Genetics and Molecular Biology
|June 30, 2012
PubMed
Summary

This study introduces a novel Bayesian model to identify genes with monotonic expression trends in time-course microarray data. The model aids in discovering genes involved in biological processes like embryo development.

More Related Videos

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions
11:22

Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions

Published on: January 30, 2018

Related Experiment Videos

Last Updated: May 20, 2026

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions
11:22

Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions

Published on: January 30, 2018

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Time-course microarray data analysis is crucial for understanding dynamic biological processes.
  • Identifying genes with monotonic trends (increasing or decreasing expression) is key to pinpointing genes involved in developmental stages or treatment responses.
  • Previous models may lack the flexibility to capture complex gene expression dynamics.

Purpose of the Study:

  • To propose a flexible Bayesian autoregressive hidden Markov model for analyzing time-course microarray data.
  • To identify genes exhibiting monotonic trends in expression over time.
  • To provide decision criteria for gene selection based on model parameter uncertainty.

Main Methods:

  • Development of a novel Bayesian autoregressive hidden Markov model with three latent states: stationarity, increasing trend, and decreasing trend.
  • Utilizing decision criteria based on the posterior distribution of model parameters for gene selection.
  • Comparison of the proposed model against simpler, constrained models.

Main Results:

  • The proposed model offers a flexible framework for identifying monotonic gene expression trends.
  • Decision criteria effectively incorporate parameter uncertainty for robust gene list selection.
  • The model demonstrates potential in uncovering genes related to developmental processes, such as mouse embryo development with 22q11 deletion.

Conclusions:

  • The novel Bayesian model provides an effective and flexible approach for analyzing time-course gene expression data.
  • The proposed method enhances the identification of genes with significant monotonic trends, aiding in biological discovery.
  • This approach offers improved insights into gene regulation during development and in response to stimuli.