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 Experiment Videos

DeepLoc: prediction of protein subcellular localization using deep learning.

José Juan Almagro Armenteros1,2, Casper Kaae Sønderby2, Søren Kaae Sønderby2

  • 1Department of Bio and Health Informatics, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark.

Bioinformatics (Oxford, England)
|October 17, 2017
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Fatigue, neurological, and cognitive symptoms after COVID-19 - a nationwide matched cohort study in Denmark.

Infectious diseases (London, England)·2026
Same author

Time to HIV rebound after infusion of long-acting broadly neutralising antibodies 3BNC117-LS and 10-1074-LS and analytical treatment interruption (the RIO trial): a double-blind, randomised, placebo-controlled trial.

The lancet. HIV·2026
Same author

A comparison of deep multiomics profiles across ethnicity, geography, and age.

Cell·2026
Same author

Early vs late diagnosis in infectious encephalitis: a population-based cohort study.

Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases·2026
Same author

Infectious encephalitis among adults: a prospective and population-based cohort study.

Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases·2026
Same author

Fecal Mycobiota in Patients with Inflammatory Bowel Diseases and Extraintestinal Manifestations.

Gut microbes reports·2026
Same journal

Biomedical Concept Recognition with Error-aware Negative-enhanced Ranking Framework.

Bioinformatics (Oxford, England)·2026
Same journal

TEDLH: Domain HMMs for sensitive detection of remote homologues.

Bioinformatics (Oxford, England)·2026
Same journal

PLNFGL: Joint Estimation of Multi-Condition Gene Networks from Single-cell RNA-seq Data.

Bioinformatics (Oxford, England)·2026
Same journal

MCFST: Spatial domain identification method based on multi-view graph convolutional network and graph fusion network.

Bioinformatics (Oxford, England)·2026
Same journal

SpaBiT: Enhancing Spatial Transcriptomics Resolution via Bidirectional Attention Transformers.

Bioinformatics (Oxford, England)·2026
Same journal

EDEL: Enhancing Dense Retrievers for Curation of Biomedical Knowledge Bases.

Bioinformatics (Oxford, England)·2026
See all related articles

This study introduces a deep neural network model for predicting protein subcellular localization using only amino acid sequences. The novel algorithm achieves high accuracy, outperforming existing methods for identifying protein location within cells.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Proteomics

Background:

  • Predicting eukaryotic protein subcellular localization is crucial for proteomics.
  • Current methods often rely on homologous protein annotations.
  • Predicting localization from sequence alone is needed for novel proteins and variants.

Purpose of the Study:

  • To develop a deep learning model for predicting protein subcellular localization using only sequence information.
  • To overcome limitations of homology-based prediction methods.

Main Methods:

  • Utilized deep neural networks, specifically a recurrent neural network with an attention mechanism.
  • Trained and tested the model on a stringent dataset from a recent UniProt release.

Related Experiment Videos

Main Results:

  • The model accurately predicts protein subcellular localization based solely on sequence.
  • Achieved 78% accuracy across 10 categories and 92% for soluble vs. membrane-bound.
  • Outperformed current state-of-the-art prediction algorithms.

Conclusions:

  • The developed deep learning approach provides a powerful tool for predicting protein subcellular localization.
  • This sequence-based method offers an advantage for novel proteins lacking homology data.