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

Classification of Neurotransmitters

Classification of Neurotransmitters

Neurotransmitters play a crucial role in the communication between neurons in the autonomic nervous system. Neurons in the autonomic nervous system can be cholinergic or adrenergic depending on the neurotransmitters synthesized. Cholinergic neurons use acetylcholine as their primary neurotransmitter. This includes all the preganglionic fibers of the sympathetic and pre- and postganglionic fibers of the parasympathetic nervous systems. In addition, neurons of the somatic nervous system also use...

You might also read

Related Articles

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

Sort by

Same author

Accurate Protein Submitochondrial Localization Using Pre-Training and Adaptive Sampling Enhanced Deep Learning Model.

IEEE journal of biomedical and health informatics·2026

Same author

Concurrent Optimization of Device Architecture, Transport Layers, and Active Layer for Organic Photovoltaics by Machine Learning.

The journal of physical chemistry letters·2026

Same author

Innovative 3D structured light technology for robotic-assisted surgery in selected spontaneous medium-volume basal ganglia hematomas: enhancing precision, surgical nuance, and clinical outcomes.

BMC surgery·2026

Same author

Navigating high-dimensional processing parameters in organic photovoltaics via a multitier machine learning framework.

Science advances·2026

Same author

Tenecteplase for Acute Non-Large Vessel Occlusion 4.5 to 24 Hours After Ischemic Stroke: The OPTION Randomized Clinical Trial.

JAMA·2026

Same author

The relationship between place attachment and pro-environmental behavioral intentions: the mediating role of nature connectedness.

Frontiers in psychology·2026

Same journal

Exploring Spectral Graph Theory in Combinatorial Chemistry.

Combinatorial chemistry & high throughput screening·2026

Same journal

Unveiling the Cellular and Molecular Insights into Ulcerative Colitis Pathogenesis through Integrative Multi-omics and Functional Experiments.

Combinatorial chemistry & high throughput screening·2026

Same journal

Integrated Multi-Omics Identification of Novel Diagnostic Biomarkers and Immunometabolic Therapeutic Targets in Osteoporosis via Machine Learning.

Combinatorial chemistry & high throughput screening·2026

Same journal

Exploration and Prospects of Core Research Hotspots in the Mechanisms of Traditional Chinese Medicine for Treating Digestive System Malignancies Based on Bibliometrics.

Combinatorial chemistry & high throughput screening·2026

Same journal

Investigating the Potential Pharmacological Mechanisms of Huangqi in Allergic Rhinitis Using Network Pharmacology and Molecular Docking Strategies.

Combinatorial chemistry & high throughput screening·2026

Same journal

DNMT3A-driven Clonal Expansion of Hematopoiesis Influences Autoimmunity Mediated by Distinct Immune Cell Populations: A Mendelian Randomization Study.

Combinatorial chemistry & high throughput screening·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Dec 27, 2025

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

Antioxidant Proteins' Identification Based on Support Vector Machine.

Yuanke Xu¹, Yaping Wen¹, Guosheng Han¹

¹School of Mathematics and Computational Science, Xiangtan University, Hunan, China.

Combinatorial Chemistry & High Throughput Screening

|March 7, 2020

Summary

This summary is machine-generated.

This study introduces a computational method using support vector machines to accurately identify antioxidant proteins, crucial for understanding aging and disease. The developed model achieved high prediction accuracy, outperforming existing methods.

Keywords:

5-fold crossvalidation 9-gap dipeptide SVM antioxidant proteins non-antioxidant proteins principal component analysis.

More Related Videos

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

A Generalized Method for Determining Free Soluble Phenolic Acid Composition and Antioxidant Capacity of Cereals and Legumes

A Generalized Method for Determining Free Soluble Phenolic Acid Composition and Antioxidant Capacity of Cereals and Legumes

Published on: June 10, 2022

Related Experiment Videos

Last Updated: Dec 27, 2025

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

A Generalized Method for Determining Free Soluble Phenolic Acid Composition and Antioxidant Capacity of Cereals and Legumes

A Generalized Method for Determining Free Soluble Phenolic Acid Composition and Antioxidant Capacity of Cereals and Legumes

Published on: June 10, 2022

Area of Science:

Biochemistry
Computational Biology
Proteomics

Background:

Cell metabolism and protein dysregulation are linked to human diseases.
Free radicals cause cellular and DNA damage.
Antioxidant proteins protect against free radical damage, making their identification vital for health research.

Purpose of the Study:

To develop and evaluate a computational method for identifying antioxidant proteins.
To leverage machine learning for accurate and efficient prior-pinpointing of antioxidant proteins for experimental verification.

Main Methods:

Utilized support vector machines (SVM) for protein identification.
Employed amino acid composition and 9-gap dipeptide composition for feature extraction.
Applied Principal Component Analysis (PCA) for feature reduction.

Main Results:

Achieved a prediction accuracy of 98.38%, with a positive sample recall (Sn) of 99.27% and negative sample recall (Sp) of 97.54%.
The model correctly predicted all 20 antioxidant proteins from the National Center for Biotechnology Information (NCBI) dataset.
Demonstrated superior performance compared to state-of-the-art methods for antioxidant protein identification.

Conclusions:

The developed SVM model, using amino acid and 9-gap dipeptide features, effectively identifies antioxidant proteins.
The method shows significant utility for recognizing antioxidant proteins, aiding in disease research and therapeutic development.