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

Protein Networks02:26

Protein Networks

4.5K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.5K
Protein Networks02:26

Protein Networks

2.8K
2.8K
Predicting Molecular Geometry02:27

Predicting Molecular Geometry

45.6K
VSEPR Theory for Determination of Electron Pair Geometries
45.6K
Subcellular Fractionation01:32

Subcellular Fractionation

8.7K
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...
8.7K
Network Covalent Solids02:18

Network Covalent Solids

16.1K
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
16.1K
Location and Orientation of the Heart01:13

Location and Orientation of the Heart

9.6K
The human heart, despite its modest size and weight, is an organ of remarkable strength and endurance. Roughly the size of a fist, the heart weighs between 250 and 350 grams and is nestled within the mediastinum, the medial cavity of the thorax. It extends obliquely for about 12 to 14 cm, resting on the superior surface of the diaphragm. The heart is positioned anterior to the vertebral column and posterior to the sternum, with two-thirds of its mass lying to the left of the midsternal line.
9.6K

You might also read

Related Articles

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

Sort by
Same author

Interactions of the Biphenylene Network with α-Helical and β-Sheet Proteins: Molecular Dynamics Simulations.

Langmuir : the ACS journal of surfaces and colloids·2024
Same author

[Study of electroreflectance spectrum and Franz-Keldysh effect at metal-GaAs interfaces].

Guang pu xue yu guang pu fen xi = Guang pu·2008
Same author

Comparison of the curative effects of video assisted thoracoscopic anterior correction and small incision, thoracotomic anterior correction for idiopathic thoracic scoliosis.

Chinese medical journal·2008
Same author

Distribution and sources of mercury in soils from former industrialized urban areas of Beijing, China.

Environmental monitoring and assessment·2008
Same author

[Main flavonoids from Sophora flavescenes].

Yao xue xue bao = Acta pharmaceutica Sinica·2008
Same author

External validation and prediction employing the predictive squared correlation coefficient test set activity mean vs training set activity mean.

Journal of chemical information and modeling·2008

Related Experiment Video

Updated: Jan 24, 2026

A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

69.7K

Bioimage-Based Prediction of Protein Subcellular Location in Human Tissue with Ensemble Features and Deep Networks.

Guang-Hui Liu, Bei-Wei Zhang, Gang Qian

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |May 21, 2019
    PubMed
    Summary

    This study presents a novel method for predicting protein subcellular location using multi-view image features. Combining deep learning with traditional methods improves accuracy for disease mechanism and drug design insights.

    More Related Videos

    Deep Neural Networks for Image-Based Dietary Assessment
    13:19

    Deep Neural Networks for Image-Based Dietary Assessment

    Published on: March 13, 2021

    9.9K
    Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
    08:49

    Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

    Published on: June 20, 2025

    1.2K

    Related Experiment Videos

    Last Updated: Jan 24, 2026

    A Protocol for Computer-Based Protein Structure and Function Prediction
    16:41

    A Protocol for Computer-Based Protein Structure and Function Prediction

    Published on: November 3, 2011

    69.7K
    Deep Neural Networks for Image-Based Dietary Assessment
    13:19

    Deep Neural Networks for Image-Based Dietary Assessment

    Published on: March 13, 2021

    9.9K
    Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
    08:49

    Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

    Published on: June 20, 2025

    1.2K

    Area of Science:

    • Bioinformatics
    • Computational Biology
    • Cell Biology

    Background:

    • Protein subcellular location prediction is crucial for understanding disease mechanisms and drug design.
    • Automated microscopic imaging generates complex bioimages, necessitating advanced classification methods.
    • Existing methods require improvement for accurate protein distribution analysis.

    Purpose of the Study:

    • To develop an accurate prediction method for protein subcellular location using bioimage analysis.
    • To integrate multi-view image features for enhanced localization prediction.
    • To combine deep learning with traditional classification for improved performance.

    Main Methods:

    • Extraction of multi-view image features: original image textures, protein channel features (global/local), and DNA channel features (global).
    • Feature fusion to create ensemble features for improved prediction.
    • Development of a hybrid classifier combining Stacked Auto-encoders (deep learning) and Random Forest (traditional classification).

    Main Results:

    • The proposed method achieved high accuracy in predicting protein subcellular location.
    • Ensemble features derived from multi-view data significantly improved prediction performance.
    • The hybrid deep learning and traditional classifier approach demonstrated superior efficacy.

    Conclusions:

    • The multi-view feature extraction and hybrid classification approach is effective for protein subcellular localization.
    • This method offers a valuable tool for biological research, aiding in disease mechanism studies and drug discovery.
    • The findings highlight the potential of integrating diverse image features and advanced computational techniques.