Jove
Visualize
Contact Us

Related Concept Videos

Amyloid Fibrils03:03

Amyloid Fibrils

9.2K
Amyloid fibrils are aggregates of misfolded proteins.  Under most circumstances, misfolded proteins are either refolded by chaperone proteins or degraded by the proteasome. However, in the case of a mutation or a disease, these proteins can accumulate to form large clusters and often further assemble to form elongated fibers, called fibrils. 
Amyloid deposits were observed as early as 1639 in the liver and the spleen.   In 1854, Rudolph Virchow performed iodine staining,...
9.2K
Alzheimer's Disease: Overview01:26

Alzheimer's Disease: Overview

412
Alzheimer's Disease (AD) is a continually advancing neurodegenerative disorder, distinguished by escalating memory loss, cognitive dysfunction, and dementia. The disease unfolds in three stages: preclinical, mild cognitive impairment (MCI), and dementia. Its onset is insidious, and the progression gradual, with the cause not well explained by other disorders.
The clinical diagnosis of AD hinges on the presence of memory and other cognitive impairments. Biomarkers, such as changes in Aβ...
412

You might also read

Related Articles

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

Sort by
Same author

Asymmetric gating of a homopentameric ion channel GLIC revealed by cryo-EM.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same author

An incomplete multiview clustering approach considering missing data recovery based on consistency.

Neural networks : the official journal of the International Neural Network Society·2025
Same author

Prolonged Exposure to Elevated Iodine Levels in Drinking Water Is Associated With the Occurrence of Autoimmune Thyroid Disorders in Adults: Findings From a Case-Control Study Conducted in Shandong Province, China.

Journal of nutrition and metabolism·2025
Same author

Comparative single-nucleus RNA-seq analysis revealed localized and cell type-specific pathways governing root-microbiome interactions.

Nature communications·2025
Same author

Application of nail analysis in human biomonitoring of toxic pollutants: A review.

Environmental pollution (Barking, Essex : 1987)·2025
Same author

Automatic magnetic solid phase extraction for rapid and high-throughput determination of neonicotinoid insecticides and their metabolites in serum, breast milk and urine samples.

Analytical methods : advancing methods and applications·2024
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 Video

Updated: May 23, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

935

Predicting amyloid proteins using attention-based long short-term memory.

Zhuowen Li1

  • 1Punan Branch of Renji Hospital, Shanghai Jiao Tong University, Shanghai, China.

Peerj. Computer Science
|March 10, 2025
PubMed
Summary

This study introduces a new computational model for identifying amyloid proteins, crucial for Alzheimer's disease (AD) research. The model effectively uses sequence information, outperforming existing methods in early disease detection.

Keywords:
AlzheimerAmyloidAttentionDeep learningLSTMTransformers

More Related Videos

Detecting Amyloid-β Accumulation via Immunofluorescent Staining in a Mouse Model of Alzheimer's Disease
08:25

Detecting Amyloid-β Accumulation via Immunofluorescent Staining in a Mouse Model of Alzheimer's Disease

Published on: April 19, 2021

3.2K
Selection of Aptamers for Amyloid β-Protein, the Causative Agent of Alzheimer's Disease
15:23

Selection of Aptamers for Amyloid β-Protein, the Causative Agent of Alzheimer's Disease

Published on: May 13, 2010

19.3K

Related Experiment Videos

Last Updated: May 23, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

935
Detecting Amyloid-β Accumulation via Immunofluorescent Staining in a Mouse Model of Alzheimer's Disease
08:25

Detecting Amyloid-β Accumulation via Immunofluorescent Staining in a Mouse Model of Alzheimer's Disease

Published on: April 19, 2021

3.2K
Selection of Aptamers for Amyloid β-Protein, the Causative Agent of Alzheimer's Disease
15:23

Selection of Aptamers for Amyloid β-Protein, the Causative Agent of Alzheimer's Disease

Published on: May 13, 2010

19.3K

Area of Science:

  • Neuroscience
  • Computational Biology
  • Biochemistry

Background:

  • Alzheimer's disease (AD) is a neurodegenerative disorder linked to amyloid protein accumulation.
  • Early identification of amyloid proteins is vital for treating AD and related diseases.
  • Existing machine learning methods for amyloid identification often underutilize protein sequence data.

Purpose of the Study:

  • To develop an advanced computational model for the in silico identification of amyloid proteins.
  • To improve the accuracy of amyloid protein detection by leveraging sequence information more effectively.
  • To provide a tool for early diagnosis and treatment strategies for Alzheimer's disease.

Main Methods:

  • Development of a computational model using bidirectional long short-term memory (BiLSTM).
  • Integration of an attention mechanism to enhance sequence information processing.
  • In silico testing and performance evaluation against state-of-the-art methods.

Main Results:

  • The proposed model demonstrated superior performance in amyloid protein identification.
  • Achieved an area under the receiver operating characteristic curve (AUC) of 0.9126.
  • Outperformed existing state-of-the-art methods in identifying amyloid proteins.

Conclusions:

  • The developed BiLSTM model with an attention mechanism is highly effective for in silico amyloid protein identification.
  • This approach significantly improves upon current machine learning techniques by better utilizing sequence data.
  • The findings offer a promising tool for early detection and management of Alzheimer's disease.