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

Genomics02:02

Genomics

41.7K
Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
41.7K
Parallel Processing01:20

Parallel Processing

873
The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
873
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

7.2K
Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
7.2K
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

16.6K
Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
16.6K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

310
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...
310
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

393
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...
393

You might also read

Related Articles

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

Sort by
Same authorSame journal

Annealed variational mixtures for disease subtyping and biomarker discovery.

Statistical applications in genetics and molecular biology·2026
Same author

Semi-supervised Bayesian integration of multiple spatial proteomics datasets.

PLoS computational biology·2025
Same author

Outcome-guided spike-and-slab Lasso Biclustering: A Novel Approach for Enhancing Biclustering Techniques for Gene Expression Analysis.

Statistics and computing·2025
Same author

VICatMix: variational Bayesian clustering and variable selection for discrete biomedical data.

Bioinformatics advances·2025
Same author

Dynamic factor analysis with dependent Gaussian processes for high-dimensional gene expression trajectories.

Biometrics·2024
Same author

Bayesian clustering with uncertain data.

PLoS computational biology·2024
Same journal

Performance of the permutation test approach with base calling errors for detecting changes in variant allele frequencies in ctDNA for a single patient.

Statistical applications in genetics and molecular biology·2026
Same journal

BLOG: Bayesian longitudinal omics with group constraints.

Statistical applications in genetics and molecular biology·2026
Same journal

AI-driven risk prediction and categorization in cystic fibrosis leveraging AttentiveLSTM and Fox Wolf Optimizer.

Statistical applications in genetics and molecular biology·2026
Same journal

Perfect collinearity not created equal: measuring and visualizing the severity of multi-collinearity of modern omics data.

Statistical applications in genetics and molecular biology·2026
Same journal

Corrigendum to: Choice of baseline hazards in joint modeling of longitudinal and time-to-event cancer survival data.

Statistical applications in genetics and molecular biology·2025
See all related articles

Related Experiment Video

Updated: Mar 25, 2026

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

2.9K

MDI-GPU: accelerating integrative modelling for genomic-scale data using GP-GPU computing.

Samuel A Mason, Faiz Sayyid, Paul D W Kirk

    Statistical Applications in Genetics and Molecular Biology
    |February 25, 2016
    PubMed
    Summary
    This summary is machine-generated.

    We developed a faster Bayesian correlated clustering algorithm for integrating multi-dimensional systems biology data. This enhanced computational performance enables routine analysis of large genomic-scale datasets.

    More Related Videos

    Novel 3D/VR Interactive Environment for MD Simulations, Visualization and Analysis
    11:29

    Novel 3D/VR Interactive Environment for MD Simulations, Visualization and Analysis

    Published on: December 18, 2014

    12.4K
    Mapping Mammalian 3D Genome Interactions with Micro-C-XL
    11:41

    Mapping Mammalian 3D Genome Interactions with Micro-C-XL

    Published on: November 3, 2023

    3.9K

    Related Experiment Videos

    Last Updated: Mar 25, 2026

    Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
    08:03

    Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

    Published on: December 7, 2021

    2.9K
    Novel 3D/VR Interactive Environment for MD Simulations, Visualization and Analysis
    11:29

    Novel 3D/VR Interactive Environment for MD Simulations, Visualization and Analysis

    Published on: December 18, 2014

    12.4K
    Mapping Mammalian 3D Genome Interactions with Micro-C-XL
    11:41

    Mapping Mammalian 3D Genome Interactions with Micro-C-XL

    Published on: November 3, 2023

    3.9K

    Area of Science:

    • Systems Biology
    • Genomic Medicine
    • Computational Biology

    Background:

    • Integrating multi-dimensional datasets is crucial but challenging in systems biology and genomic medicine.
    • High-throughput technologies yield diverse data types, increasing computational burden for inference.
    • Bayesian methods offer flexibility but can be computationally intensive.

    Purpose of the Study:

    • To present an improved implementation of a Bayesian correlated clustering algorithm.
    • To enable routine integrated clustering across multiple large-scale datasets.
    • To significantly enhance computational performance for analyzing genomic data.

    Main Methods:

    • Developed an improved Bayesian correlated clustering algorithm.
    • Implemented GPU-based computation to accelerate the algorithm.
    • Applied the algorithm to multi-dimensional datasets with tens of thousands of items.

    Main Results:

    • Achieved a runtime performance improvement of almost four orders of magnitude.
    • Enabled routine integrated clustering across multiple genomic-scale datasets.
    • Expanded the applicability of Bayesian correlated clustering to larger and more complex data.

    Conclusions:

    • The improved algorithm facilitates routine integration of multi-dimensional genomic data.
    • GPU acceleration makes large-scale systems biology analyses computationally feasible.
    • This advancement significantly broadens the scope of integrated data analysis in genomic medicine.