Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Protein and Protein Structure

Protein and Protein Structure

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 can...

Antibody Structure and Classes

Antibody Structure and Classes

Antibodies, also known as immunoglobulins, are produced by B cells in response to foreign substances, such as bacteria and viruses. These proteins are critical for recognizing and neutralizing these substances, protecting the body from potential harm.
The basic structure of an antibody consists of four protein chains: two identical heavy chains and two identical light chains. These chains are held together by disulfide bonds and other non-covalent interactions, forming a Y-shaped structure.

Conservation of Protein Domains Over Different Proteins

Conservation of Protein Domains Over Different Proteins

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 form...

Protein Complexes with Interchangeable Parts

Protein Complexes with Interchangeable Parts

Groups of proteins may form a complex where each protein in this complex has a different role in the overall execution of the complex’s function. Often some of the proteins in the complex can be replaced by a closely related variant to give a complex that contains many of the same components yet is functionally distinct.
The SCF ubiquitin ligase is a protein complex of five individual proteins. This complex attaches ubiquitin to other target proteins to mark them for degradation. In order to...

Protein Complexes with Interchangeable Parts

Protein Complexes with Interchangeable Parts

Groups of proteins may form a complex where each protein in this complex has a different role in the overall execution of the complex’s function. Often some of the proteins in the complex can be replaced by a closely related variant to give a complex that contains many of the same components yet is functionally distinct.
The SCF ubiquitin ligase is a protein complex of five individual proteins. This complex attaches ubiquitin to other target proteins to mark them for degradation. In order to...

Protein and Protein Structures

Protein and Protein Structures

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 can...

You might also read

Related Articles

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

Sort by

Same author

More Efforts Needed to Clarify the Effect of Lithium in Bipolar Disorder: Response to Terao et al.

The American journal of psychiatry·2018

Same author

Di-branched triphenylamine dye sensitized TiO<sub>2</sub> nanocomposites with good photo-stability for sensitive photoelectrochemical detection of organophosphate pesticides.

Analytica chimica acta·2018

Same author

pH-responsive gold nanoclusters-based nanoprobes for lung cancer targeted near-infrared fluorescence imaging and chemo-photodynamic therapy.

Acta biomaterialia·2018

Same author

[Ideas and methods on efficient screening of traditional medicines for anti-osteoporosis activity based on M-Act/Tox integrated evaluation using zebrafish].

Zhongguo Zhong yao za zhi = Zhongguo zhongyao zazhi = China journal of Chinese materia medica·2017

Same author

Ceftazidime-avibactam versus meropenem in nosocomial pneumonia, including ventilator-associated pneumonia (REPROVE): a randomised, double-blind, phase 3 non-inferiority trial.

The Lancet. Infectious diseases·2017

Same author

Myricetin against ischemic cerebral injury in rat middle cerebral artery occlusion model.

Molecular medicine reports·2017

Search research articles

Related Experiment Video

Updated: Jun 25, 2026

A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Multiple classifier integration for the prediction of protein structural classes.

Lei Chen¹, Lin Lu, Kairui Feng

¹Shanghai Key Laboratory of Trustworthy Computing, East China Normal University, Shanghai 200062, People's Republic of China.

Journal of Computational Chemistry

|March 11, 2009

Summary

This summary is machine-generated.

Integrating multiple supervised classifiers improves biological data prediction. Combining algorithms using weighted voting and feature selection enhanced accuracy over individual methods, achieving over 69% correct predictions.

More Related Videos

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

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

Published on: June 6, 2025

Related Experiment Videos

Last Updated: Jun 25, 2026

A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

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

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

Published on: June 6, 2025

Area of Science:

Bioinformatics
Machine Learning
Computational Biology

Background:

Supervised classifiers are crucial for biological data analysis but selecting the optimal one is challenging due to varying dataset characteristics and classifier strengths.
Ensemble methods, by integrating multiple classifiers, offer a way to overcome the limitations of individual models and achieve better classification performance.

Purpose of the Study:

To develop and evaluate an ensemble strategy for biological data classification that integrates multiple machine learning algorithms.
To improve prediction accuracy by addressing classifier redundancy and optimizing classifier weightings.

Main Methods:

Utilized Weka software for a collection of machine learning classification algorithms.
Implemented a simple majority voting ensemble, achieving initial prediction rates of 65.21% (training) and 65.63% (testing).
Introduced an advanced integration strategy incorporating classifier weightings and a minimum redundancy maximum relevance (mRMR) feature selection method to manage classifier redundancy.

Main Results:

The simple voting ensemble outperformed individual Weka algorithms on the same dataset.
The optimized ensemble, using 11 algorithms selected by mRMR and integrated with weighted majority voting, achieved the highest prediction accuracy.
The best results showed correct prediction rates of 68.56% for the training dataset and 69.29% for the independent test dataset.

Conclusions:

Ensemble methods, particularly those that consider classifier weighting and redundancy, significantly enhance biological data classification accuracy.
The developed integration strategy provides a robust approach to optimizing classifier combinations for improved predictive performance.
The findings suggest that ensemble learning is a powerful tool for complex biological data analysis, with a web server available for practical application.