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

Protein Dynamics in Living Cells

Protein Dynamics in Living Cells

Different fluorescence-based techniques are used to study the protein dynamics in living cells. These techniques include FRAP, FRET, and PET.
Fluorescent recovery after photobleaching (FRAP) is a fluorescent-protein-based detection technique used to quantify protein movement rates within the cell. This method exposes a small portion of the cell to an intense laser beam. The laser beam causes permanent photobleaching of the fluorophore-tagged proteins in the exposed region. As the bleached...

You might also read

Related Articles

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

Sort by

Same author

Fe(NO<sub>3</sub>)<sub>3</sub>·9H<sub>2</sub>O-Promoted Direct Deaminative Thiocyanation of Primary Amine.

Organic letters·2026

Same author

Preserved diaphragm thickness and tidal contractility despite motor impairment in early-stage Parkinson's disease.

Respiratory physiology & neurobiology·2026

Same author

Psycho-social-contextual landscape of return to sport after hamstring strain injuries: a scoping review.

BMJ open sport & exercise medicine·2026

Same author

Physical exercise in combination with audiovisual stimulation alleviates cognitive and affective impairments in Alzheimer's disease model mice via restoring lysosomal membrane integrity.

Alzheimer's research & therapy·2026

Same author

Serotype-specific tropism of adeno-associated viruses in dorsal meningeal lymphatic vessels via intra-cisterna magna delivery.

Frontiers in immunology·2026

Same author

Socio-Economic and Occupational Variations in Cardiovascular Risk Among Irish Male Livestock Farmers: Baseline Findings from the Farmers Have Hearts-Cardiovascular Health Program.

Journal of agromedicine·2026

Same journal

Evaluation of temporal preservation in synthetic longitudinal patient data.

Journal of biomedical informatics·2026

Same journal

ARKE: An ontology-driven framework for automated mapping of local radiology procedure terms to the LOINC-RadLex playbook using large language model.

Journal of biomedical informatics·2026

Same journal

A validation-driven training controller for cross-lingual biomedical NER via reinforcement learning-based adaptive loss weighting.

Journal of biomedical informatics·2026

Same journal

ASP-HR: An Adaptive Spatial Perception and Hierarchical Reasoning mechanism for document-level biomedical relation extraction.

Journal of biomedical informatics·2026

Same journal

Beyond Accuracy: Safety-Centered guidelines for the evaluation of LLM-based therapy recommendation systems for chronic multimorbidity patients.

Journal of biomedical informatics·2026

Same journal

DeepEN: A deep reinforcement learning framework for personalized enteral nutrition in critical care.

Journal of biomedical informatics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Apr 6, 2026

Multi-color Localization Microscopy of Single Membrane Proteins in Organelles of Live Mammalian Cells

Multi-color Localization Microscopy of Single Membrane Proteins in Organelles of Live Mammalian Cells

Published on: June 30, 2018

Using distant supervised learning to identify protein subcellular localizations from full-text scientific articles.

Wu Zheng¹, Catherine Blake²

¹Graduate School of Library and Information Science, University of Illinois at Urbana-Champaign, USA.

Journal of Biomedical Informatics

|July 30, 2015

Summary

This summary is machine-generated.

Automated methods using distant supervised learning can identify protein subcellular localizations from scientific articles, aiding protein function research and drug target discovery.

Keywords:

BioNLP Distant supervised learning Protein subcellular localization extraction Relation extraction Text mining

More Related Videos

Detecting Protein Subcellular Localization by Green Fluorescence Protein Tagging and 4',6-Diamidino-2-phenylindole Staining in Caenorhabditis elegans

Detecting Protein Subcellular Localization by Green Fluorescence Protein Tagging and 4',6-Diamidino-2-phenylindole Staining in Caenorhabditis elegans

Published on: July 30, 2018

Enriching Subcellular Proteins in Leptospira Using a Triton X-114-Based Fractionation Approach

Enriching Subcellular Proteins in Leptospira Using a Triton X-114-Based Fractionation Approach

Published on: August 8, 2025

Related Experiment Videos

Last Updated: Apr 6, 2026

Multi-color Localization Microscopy of Single Membrane Proteins in Organelles of Live Mammalian Cells

Multi-color Localization Microscopy of Single Membrane Proteins in Organelles of Live Mammalian Cells

Published on: June 30, 2018

Detecting Protein Subcellular Localization by Green Fluorescence Protein Tagging and 4',6-Diamidino-2-phenylindole Staining in Caenorhabditis elegans

Detecting Protein Subcellular Localization by Green Fluorescence Protein Tagging and 4',6-Diamidino-2-phenylindole Staining in Caenorhabditis elegans

Published on: July 30, 2018

Enriching Subcellular Proteins in Leptospira Using a Triton X-114-Based Fractionation Approach

Enriching Subcellular Proteins in Leptospira Using a Triton X-114-Based Fractionation Approach

Published on: August 8, 2025

Area of Science:

Biomedical Informatics
Computational Biology
Molecular Biology

Background:

Curated biomedical knowledge bases, like UniProtKB, are crucial for research.
Manual curation of these databases is time-consuming and benefits from automation.
Understanding protein subcellular localization is key to protein function and drug discovery.

Purpose of the Study:

To develop and evaluate an automated approach for identifying protein subcellular localizations.
To leverage full-text scientific articles to augment manual knowledge base curation.
To assess the utility of distant supervised learning for this task.

Main Methods:

Utilized distant supervised learning on a large corpus of full-text articles (Journal of Biological Chemistry).
Focused on the Swiss-Prot subset of the UniProtKB database for training and evaluation.
Processed approximately 11.5 million sentences from 43,000 articles.

Main Results:

Achieved 0.81 precision and 0.49 recall at the sentence level.
Obtained 57% accuracy on held-out test instances.
Identified 8210 novel protein localization instances not present in UniProtKB, with 82% validated manually.

Conclusions:

Distant supervised learning is a viable method to automate protein localization extraction from literature.
This approach can significantly augment existing biomedical knowledge bases.
The findings offer immediate benefits for researchers studying protein function and seeking drug targets.