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

Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

14.6K
Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to...
14.6K
Protein Networks02:26

Protein Networks

4.6K
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,...
4.6K
Network Covalent Solids02:18

Network Covalent Solids

16.2K
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...
16.2K
Membrane Domains01:18

Membrane Domains

7.2K
The membrane domains concentrate specific lipids and proteins at one place within the membrane, which helps in cell signaling, adhesion, and other critical cellular processes. These domains can differ in size, composition, function, and lifespan.
Protein Domains
The membrane comprises a group of distinct proteins responsible for carrying out a cell's specific function. For example, the plasma membrane of the human sperm, or a single germ cell, contains a unique set of proteins in the...
7.2K
Three Developmental Domains01:29

Three Developmental Domains

1.1K
Human development is typically examined across three main domains: physical, cognitive, and socio-emotional. These domains represent the significant areas of change and continuity throughout the lifespan, from infancy to late adulthood.
Physical Development
Physical processes, also known as maturation, encompass the biological changes that occur across an individual's life. These changes begin with genetic inheritance and continue through various stages, including growth in height and weight,...
1.1K
Three-Domain System of Life01:21

Three-Domain System of Life

1.3K
Ribosomal RNA (rRNA) sequence analysis revealed three distinct groups of cells: eukaryotes, bacteria, and archaea. In 1978, Carl R. Woese proposed the concept of domains, a taxonomic level above kingdoms, to differentiate these groups. He suggested that archaea and bacteria, despite their similar appearance, represent separate domains. Domains differ in rRNA, membrane lipid structure, transfer RNA, and antibiotic sensitivity.In this classification, animals, plants, and fungi belong to the...
1.3K

You might also read

Related Articles

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

Sort by
Same author

The Traditional Chinese Medicine Hua Tuo Zai Zao Wan Alleviates Atherosclerosis by Deactivation of Inflammatory Macrophages.

Evidence-based complementary and alternative medicine : eCAM·2022
Same author

Antihyperglycemic effect of an anthocyanin, cyanidin-3-<i>O</i>-glucoside, is achieved by regulating GLUT-1 <i>via</i> the Wnt/β-catenin-WISP1 signaling pathway.

Food & function·2022
Same author

Epigenetic and Transcriptional Regulation of Innate Immunity in Cancer.

Cancer research·2022
Same author

Clinical and molecular impacts of tumor mutational burden in histological and cytological specimens from cancer patients.

Annals of translational medicine·2022
Same author

Comprehensive bioinformatics analysis of functional molecules in colorectal cancer.

Journal of gastrointestinal oncology·2022
Same author

A three-staged framework for measuring water supply resilience in rural China based on PLS-SEM.

Scientific reports·2022

Related Experiment Video

Updated: Feb 8, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

8.1K

A Semisupervised Classification Approach for Multidomain Networks With Domain Selection.

Chuan Chen, Jingxue Xin, Yong Wang

    IEEE Transactions on Neural Networks and Learning Systems
    |July 12, 2018
    PubMed
    Summary

    This study introduces a novel multidomain classification with domain selection (MCS) method for enhanced network analysis. MCS effectively integrates diverse data representations and identifies relevant domains, improving prediction accuracy and network partitioning.

    More Related Videos

    A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
    12:18

    A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

    Published on: January 11, 2020

    8.1K
    Global Identification of Co-Translational Interaction Networks by Selective Ribosome Profiling
    06:58

    Global Identification of Co-Translational Interaction Networks by Selective Ribosome Profiling

    Published on: October 7, 2021

    3.0K

    Related Experiment Videos

    Last Updated: Feb 8, 2026

    Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
    07:35

    Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

    Published on: October 11, 2018

    8.1K
    A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
    12:18

    A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

    Published on: January 11, 2020

    8.1K
    Global Identification of Co-Translational Interaction Networks by Selective Ribosome Profiling
    06:58

    Global Identification of Co-Translational Interaction Networks by Selective Ribosome Profiling

    Published on: October 7, 2021

    3.0K

    Area of Science:

    • Data Science
    • Machine Learning
    • Network Analysis

    Background:

    • Multidomain network classification integrates information from multiple sources to enhance prediction performance.
    • Existing methods often assume shared instance sets across domains, limiting applicability to real-world scenarios with distinct instance sets.
    • Selecting relevant domains is crucial due to data source growth and potential domain irrelevance.

    Purpose of the Study:

    • To propose a semisupervised classification approach for multidomain networks that handles cross-domain information and varying instance sets.
    • To develop a method that automatically identifies relevant domains for improved prediction accuracy and network partitioning.
    • To address the challenge of integrating different data representations without information loss.

    Main Methods:

    • A semisupervised classification approach based on label propagation is proposed.
    • The method, multidomain classification with domain selection (MCS), utilizes sparse weight properties for automatic domain relevance identification.
    • MCS is theoretically decomposed into two simpler subproblems with analytical solutions for efficient computation.

    Main Results:

    • The proposed MCS approach demonstrates improved classification accuracy by assigning higher weights to relevant domains.
    • MCS effectively performs domain selection, identifying and prioritizing domains pertinent to the target domain.
    • Experimental results on synthetic and real-world datasets validate the approach's advantages in prediction performance and domain selection.

    Conclusions:

    • The developed MCS method offers a robust solution for multidomain network classification with distinct instance sets.
    • The approach enhances prediction accuracy and provides optimal network partitioning for the target domain through effective domain selection.
    • MCS presents a significant advancement in handling complex, heterogeneous data from multiple domains.