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

4.7K
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,...
4.7K
Protein Networks02:26

Protein Networks

2.9K
2.9K
Structural Protein Function01:56

Structural Protein Function

3.4K
3.4K
Structural Protein Function01:56

Structural Protein Function

30.5K
Structural proteins are a category of proteins responsible for functions ranging from cell shape and movement to providing support to major structures such as bones, cartilage, hair, and muscles. This group includes proteins such as collagen, actin, myosin, and keratin.
Collagen, the most abundant protein in mammals, is found throughout the body. In connective tissue, such as skin, ligaments, and tendons, it provides tensile strength and elasticity.  In bones and teeth, it mineralizes to...
30.5K
Protein and Protein Structures02:15

Protein and Protein Structures

20.1K
20.1K
Protein and Protein Structure02:15

Protein and Protein Structure

92.3K
Proteins are one of the most abundant organic molecules in living systems and have the most diverse range of functions of all macromolecules. Proteins may be structural, regulatory, contractile, or protective. They may serve in transport, storage, or membranes; or they may be toxins or enzymes. Their structures, like their functions, vary greatly. They are all, however, amino acid polymers arranged in a linear sequence.
A protein's shape is critical to its function. For example, an enzyme...
92.3K

You might also read

Related Articles

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

Sort by
Same author

Robustness evaluations of pathway activity inference methods on gene expression data.

BMC bioinformatics·2024
Same author

Graph-based extractive text summarization method for Hausa text.

PloS one·2023
Same author

An Entropy-Based Directed Random Walk for Cancer Classification Using Gene Expression Data Based on Bi-Random Walk on Two Separated Networks.

Genes·2023
Same author

HDG-select: A novel GUI based application for gene selection and classification in high dimensional datasets.

PloS one·2021
Same author

Topologically significant directed random walk with applied walker network in cancer environment.

Pakistan journal of pharmaceutical sciences·2019
Same author

A non-dominated sorting Differential Search Algorithm Flux Balance Analysis (ndsDSAFBA) for in silico multiobjective optimization in identifying reactions knockout.

Computers in biology and medicine·2019

Related Experiment Video

Updated: Mar 31, 2026

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons
08:04

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Published on: June 6, 2025

1.7K

Granular support vector machine to identify unknown structural classes of protein.

Rohayanti Hassan, Razib M Othman, Zuraini A Shah

    International Journal of Data Mining and Bioinformatics
    |October 30, 2015
    PubMed
    Summary

    A new computational method, GSVM-SigLpsSCPred, accurately classifies protein structural classes using local structures. This approach aids in determining unknown classes, especially for low-sequence-identity proteins.

    More Related Videos

    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

    70.1K
    An Integrated Approach for Microprotein Identification and Sequence Analysis
    09:37

    An Integrated Approach for Microprotein Identification and Sequence Analysis

    Published on: July 12, 2022

    4.1K

    Related Experiment Videos

    Last Updated: Mar 31, 2026

    Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons
    08:04

    Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

    Published on: June 6, 2025

    1.7K
    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

    70.1K
    An Integrated Approach for Microprotein Identification and Sequence Analysis
    09:37

    An Integrated Approach for Microprotein Identification and Sequence Analysis

    Published on: July 12, 2022

    4.1K

    Area of Science:

    • Computational Biology
    • Structural Bioinformatics
    • Machine Learning in Biology

    Background:

    • Protein structural class determination is crucial for understanding function.
    • Existing methods often rely on whole protein structures, limiting classification accuracy.
    • Local protein structure analysis is emerging as a key area for improved classification.

    Purpose of the Study:

    • To develop a novel computational method for predicting protein structural classes.
    • To address limitations of threshold-based methods in classifying unknown structural classes.
    • To leverage local protein structure features for enhanced prediction accuracy.

    Main Methods:

    • Proposed a fusion algorithm: GSVM-SigLpsSCPred (Granular Support Vector Machine--with Significant Local protein structure for Structural Class Prediction).
    • Utilized optimal local protein structures to create feature vectors.
    • Employed a granular support vector machine for classification of unknown structural classes.

    Main Results:

    • The GSVM-SigLpsSCPred algorithm demonstrated effective performance in classifying protein structural classes.
    • The method successfully predicted previously unknown structural classes.
    • Highlighted the algorithm's utility as a computational tool for low-identity sequences.

    Conclusions:

    • GSVM-SigLpsSCPred offers a robust alternative for protein structural class prediction.
    • The fusion of local structure representation and granular SVM enhances classification accuracy.
    • This method is particularly valuable for analyzing protein sequences with low identity.