Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Proteomics

Proteomics

A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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...

You might also read

Related Articles

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

Sort by

Same author

The spatial proteome of the Plasmodium falciparum schizont illuminates the composition and evolutionary trajectories of its organelles.

Nature communications·2026

Same author

Annealed variational mixtures for disease subtyping and biomarker discovery.

Statistical applications in genetics and molecular biology·2026

Same author

Association of nasopharyngeal <i>Dolosigranulum pigrum</i> and <i>Corynebacterium</i> species with post-acute sequelae of SARS-CoV-2 in a longitudinal cohort.

Microbiology spectrum·2026

Same author

Dynamic subcellular proteomics identifies regulators of adipocyte insulin action.

Nature communications·2026

Same author

Subcellular localization as a driver of protein function.

Nature reviews. Molecular cell biology·2026

Same author

Functional characterisation of tumour suppressor PDCD4 reveals previously undisclosed role in the control of cell adhesion.

Nucleic acids research·2026

Same journal

Inference on summaries of a model-agnostic longitudinal variable importance trajectory with application to suicide prevention.

The annals of applied statistics·2026

Same journal

A NOVEL BAYESIAN FRAMEWORK UNCOVERING BRAIN CONNECTIVITY-TO-SHAPE RELATIONSHIP IN PRECLINICAL ALZHEIMER'S DISEASE.

The annals of applied statistics·2026

Same journal

EVALUATING MULTIPLEX DIAGNOSTIC TEST USING PARTIALLY ORDERED BAYES CLASSIFIER.

The annals of applied statistics·2026

Same journal

BRIDGING THE GAP: ENHANCING THE GENERALIZABILITY OF EPIGENETIC CLOCKS THROUGH TRANSFER LEARNING.

The annals of applied statistics·2026

Same journal

TREATMENT EFFECT HETEROGENEITY AND IMPORTANCE MEASURES FOR MULTIVARIATE CONTINUOUS TREATMENTS.

The annals of applied statistics·2026

Same journal

FEDERATED LEARNING OF ROBUST INDIVIDUALIZED DECISION RULES WITH APPLICATION TO HETEROGENEOUS MULTIHOSPITAL SEPSIS POPULATION.

The annals of applied statistics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Aug 17, 2025

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

Semi-Supervised Non-Parametric Bayesian Modelling of Spatial Proteomics.

Oliver M Crook, Kathryn S Lilley, Laurent Gatto

The Annals of Applied Statistics

|December 12, 2022

Summary

This summary is machine-generated.

This study introduces a novel Bayesian framework for analyzing spatial proteomics data, improving understanding of protein function. The method uses Gaussian process mixtures and semi-supervised learning for accurate sub-cellular protein localization mapping.

Keywords:

Bayesian mixture models proteomics semi-supervised learning

More Related Videos

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

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

Published on: December 10, 2012

A Streamlined Approach for Mass Spectrometry-Based Proteomics Using Selected Tissue Regions

A Streamlined Approach for Mass Spectrometry-Based Proteomics Using Selected Tissue Regions

Published on: April 18, 2025

Related Experiment Videos

Last Updated: Aug 17, 2025

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

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

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

Published on: December 10, 2012

A Streamlined Approach for Mass Spectrometry-Based Proteomics Using Selected Tissue Regions

A Streamlined Approach for Mass Spectrometry-Based Proteomics Using Selected Tissue Regions

Published on: April 18, 2025

Area of Science:

Proteomics
Computational Biology
Statistical Modeling

Background:

Sub-cellular protein localization is crucial for understanding protein function.
Quantitative mass spectrometry (MS) has enabled high-resolution mapping of protein locations.
Complex spatial proteomics data requires advanced modeling techniques.

Purpose of the Study:

To develop a novel non-parametric Bayesian framework for analyzing spatial proteomics data.
To accurately model the complex correlation structures within sub-cellular niches.
To leverage marker proteins for a semi-supervised learning approach.

Main Methods:

Utilized K-component mixtures of Gaussian process regression models.
Implemented a semi-supervised learning approach using a priori known marker protein locations.
Developed an efficient Hamiltonian-within-Gibbs sampler.
Employed tensor decomposition for covariance matrices to accelerate computation via extended Trench and Durbin algorithms.

Main Results:

The proposed Bayesian framework effectively models spatial proteomics data.
Semi-supervised learning enhances the accuracy of protein localization mapping.
Computational efficiency was improved through tensor decomposition and specialized algorithms.
Demonstrated utility in case studies involving Drosophila embryos and mouse stem cells.

Conclusions:

The developed semi-supervised functional Bayesian modeling provides a powerful approach for spatial proteomics data analysis.
This method advances the accurate mapping of sub-cellular protein localization.
Offers significant computational advantages for large-scale proteomics datasets.