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

Subcellular Fractionation01:32

Subcellular Fractionation

The homogenate obtained after cell lysis contains various membrane-bound organelles that can be further separated into pure fractions by subcellular fractionation. These isolates are used to study specific cellular components, analyze localized protein activity, and are even employed in diagnostics. Fractionation is typically achieved using centrifugation methods, the most common being density-gradient and differential centrifugation.
Differential Centrifugation
Differential centrifugation is...

You might also read

Related Articles

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

Sort by
Same author

Nicotinamide-derived tumor-targeting carbon dots for Cancer Photothermal therapy.

Journal of colloid and interface science·2026
Same author

Identification of age-related biomarkers for meat quality in Kangle chickens using multi-omics.

Food chemistry. Molecular sciences·2026
Same author

Structurally diverse sesquiterpenoids from Curcuma longa and their anti-inflammatory activities.

Phytochemistry·2026
Same author

Clinical management and pharmaceutical care of severe <i>Chlamydia psittaci</i> Pneumonia complicated with rhabdomyolysis and multiple organ dysfunction: a case report and literature review.

Frontiers in cellular and infection microbiology·2026
Same author

Pulmonary metastasectomy and survival in osteosarcoma: a systematic review and meta-analysis of surgery-related prognostic factors.

World journal of surgical oncology·2026
Same author

Upregulated CCN1 from pleural mesothelial cells alters collagen I conformation and drives fibrosis via ITGB1/MAPK signaling.

Cellular & molecular biology letters·2026

Related Experiment Video

Updated: May 21, 2026

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
08:58

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

Protein subcellular location pattern classification in cellular images using latent discriminative models.

Jieyue Li1, Liang Xiong, Jeff Schneider

  • 1Center for Bioimage informatics, Department of Biomedical Engineering, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA 15213, USA.

Bioinformatics (Oxford, England)
|June 13, 2012
PubMed
Summary

We developed new models to classify protein locations in cells using microscopy images. Our method achieved 84.6% accuracy for 942 proteins, improving subcellular location prediction.

More Related Videos

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
12:06

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

Related Experiment Videos

Last Updated: May 21, 2026

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
08:58

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
12:06

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

Area of Science:

  • Cell Biology
  • Bioinformatics
  • Computational Biology

Background:

  • Understanding protein subcellular localization is vital for elucidating protein functions.
  • Microscopy images, particularly from the Human Protein Atlas (HPA), are key for determining protein localization patterns.
  • Challenges exist in inferring protein patterns when only a subset of cellular components are visible.

Purpose of the Study:

  • To develop and evaluate novel discriminative models for classifying protein subcellular patterns from confocal immunofluorescence images.
  • To improve the accuracy of protein localization prediction by considering relationships between proteins and cellular components, and their spatial interactions.

Main Methods:

  • A region-based approach was employed, extracting features from local image regions within cells.
  • Two discriminative models extending logistic regression with structured latent variables were proposed.
  • A fast approximate inference algorithm and gradient-based methods were used for model learning.

Main Results:

  • The proposed models significantly improved classification accuracy on both synthetic and real cellular image data.
  • An overall accuracy of 84.6% was achieved for classifying 942 proteins into 13 pattern classes, representing a state-of-the-art performance.
  • Learned dependencies between cellular components align with existing biological knowledge.

Conclusions:

  • The developed models offer a powerful tool for accurate protein subcellular localization prediction.
  • The region-based and structured latent variable approach effectively captures complex protein-component relationships and spatial dependencies.
  • This work advances the field of high-throughput protein localization analysis.