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

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

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, comparing...
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n) to the number of categories (k).
Statistical Methods to Analyze Parametric Data: ANOVA01:12

Statistical Methods to Analyze Parametric Data: ANOVA

Analysis of Variance, or ANOVA, is a powerful statistical technique used to analyze parametric data, primarily in research and experimental studies. It's designed to compare the means of two or more groups, assisting researchers in identifying any significant differences between these group means. There are two main types of ANOVA based on the complexity of the analysis: one-way and two-way.
One-way ANOVA is applied when a single independent variable or factor is scrutinized. It compares the...

You might also read

Related Articles

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

Sort by
Same author

Cumulative impacts of early-life interpersonal adversity and persistent/recurrent pain in children: a longitudinal normative modelling study from the ABCD study cohort.

Pain·2026
Same author

Health Care Outcomes Associated With Cognitive Super-Ageing.

The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry·2026
Same author

Transcutaneous spinal cord stimulation plus locomotor training versus sham-stimulation plus locomotor training in chronic spinal cord injury (eWALK): a multicentre, triple-blind, randomised, sham-controlled trial.

EClinicalMedicine·2026
Same author

Characteristics of Australian cognitive super-agers using different measurement approaches.

Aging & mental health·2026
Same author

White Matter Structure in Complex Regional Pain Syndrome: A High Angular Resolution and Fixel-Based Study.

European journal of pain (London, England)·2025
Same author

Long-acting intranasal insulin for the treatment of delirium-a randomised clinical trial.

Age and ageing·2025
Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026
Same journal

NAP: an open source pipeline for cross-domain microbiome profiling using Nanopore sequencing-derived amplicon data.

BMC bioinformatics·2026
Same journal

SurvGME: an R package for survival analysis with graphical and measurement error models.

BMC bioinformatics·2026
Same journal

SimMapNet: a Bayesian framework for gene regulatory network inference using gene ontology similarities as external hint.

BMC bioinformatics·2026
Same journal

Dual channel drug-drug interactions extraction based on cross attention.

BMC bioinformatics·2026
Same journal

FeSseqdb: a curated sequence-level database and interpretable machine learning framework for identifying iron-sulfur proteins.

BMC bioinformatics·2026
See all related articles

Related Experiment Video

Updated: Jul 2, 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

Parameter estimation for robust HMM analysis of ChIP-chip data.

Peter Humburg1, David Bulger, Glenn Stone

  • 1Department of Statistics, Macquarie University, North Ryde, NSW 2109, Australia. peter.humburg@csiro.au

BMC Bioinformatics
|August 19, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces an efficient hidden Markov model (HMM) parameter estimation procedure for tiling array data analysis. This method improves performance for chromatin structure studies, outperforming existing tools like TileMap.

More Related Videos

Experimental Methods to Study Human Postural Control
08:12

Experimental Methods to Study Human Postural Control

Published on: September 11, 2019

Related Experiment Videos

Last Updated: Jul 2, 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

Experimental Methods to Study Human Postural Control
08:12

Experimental Methods to Study Human Postural Control

Published on: September 11, 2019

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Tiling arrays offer high-resolution, genome-wide analysis of transcriptional activity, protein-DNA interactions, and chromatin structure.
  • Current hidden Markov model (HMM) analysis of tiling array data often uses ad hoc parameter estimation, lacking standardized procedures, especially for ChIP-chip experiments.
  • Established maximum likelihood estimation methods like Baum-Welch are seldom applied in tiling array analysis.

Purpose of the Study:

  • To develop a robust HMM for analyzing chromatin structure ChIP-chip tiling array data.
  • To implement and evaluate maximum likelihood estimation for all HMM parameters.
  • To create an efficient parameter estimation procedure for improved HMM performance.

Main Methods:

  • Development of a hidden Markov model incorporating t-distribution emission distributions for outlier robustness.
  • Application of maximum likelihood estimation for all model parameters.
  • Investigation and combination of two parameter estimation approaches into an efficient procedure.

Main Results:

  • An efficient parameter estimation procedure for HMMs was developed and demonstrated.
  • The new procedure significantly enhances performance compared to ad hoc estimation methods.
  • The developed HMM demonstrated superior performance over established methods like TileMap for histone modification studies.

Conclusions:

  • An efficient parameter estimation procedure for HMM-based tiling array analysis was established.
  • This method offers a significant performance improvement over existing ad hoc estimation techniques.
  • The HMM provides superior results for histone modification analysis compared to TileMap.