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...

Classification of Signals

Classification of Signals

In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...

Classification of Systems-I

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:

You might also read

Related Articles

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

Sort by

Same author

Heterogeneity of PD-L1 Expression Between the Primary Tumor and Matched Lymph Node Metastases in Head and Neck Squamous Cell Carcinomas.

Cancers·2026

Same author

Immunohistochemical Analysis of Potential Therapeutic Targets PRAME, FOLR1, and CLDN18.2 in Salivary Gland Carcinomas.

Cancer medicine·2026

Same author

A bibliometric analysis of the research landscape of vulvar cancer.

Discover oncology·2026

Same author

Pathologist-like explainable AI for interpretable Gleason grading in prostate cancer.

Nature communications·2025

Same author

ROS1 Expression Correlates With Inguinal Lymph Node Affection in Vulvar Cancer Patients: A Retrospective Study.

Cancer medicine·2025

Same author

Evaluation of podoplanin expression in vulvar squamous cell carcinoma and stromal fibroblasts and its prognostic implications.

Scientific reports·2025

Same journal

Mapping the 3D Chromosome Organization of a Biosynthetic Gene Cluster by Capture Hi-C (CHi-C).

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

Mapping the 3D Chromosome Organization of Streptomyces by Hi-C.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

CUT&Tag Epigenomic Profiling of Biosynthetic Gene Clusters in Arabidopsis thaliana.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

Rhizobium rhizogenes-Mediated Hairy Root Transformation Protocol for Lotus japonicus and Other Legumes.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

Characterization of Bioactive Saponins from Sea Cucumbers.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

Methods for Functional Validation of Terpenoid Metabolic Clusters in Nicotiana benthamiana and Aspergillus oryzae.

Methods in molecular biology (Clifton, N.J.)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jan 10, 2026

Classification of Neural Stem Cell Activation State In Vitro using Autofluorescence

Classification of Neural Stem Cell Activation State In Vitro using Autofluorescence

Published on: April 12, 2024

Microarray data classified by artificial neural networks.

Roland Linder¹, Tereza Richards, Mathias Wagner

¹Institute of Medical Informatics, University of Lübeck, Germany.

Methods in Molecular Biology (Clifton, N.J.)

|January 29, 2008

Summary

This summary is machine-generated.

Artificial neural networks (ANN) offer accurate analysis of microarray data for systems biology research. This review explores ANN advantages and disadvantages for classifying cell functions and tumors, comparing them to other methods.

More Related Videos

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

DNA Microarrays: Sample Quality Control, Array Hybridization and Scanning

DNA Microarrays: Sample Quality Control, Array Hybridization and Scanning

Published on: March 15, 2011

Related Experiment Videos

Last Updated: Jan 10, 2026

Classification of Neural Stem Cell Activation State In Vitro using Autofluorescence

Classification of Neural Stem Cell Activation State In Vitro using Autofluorescence

Published on: April 12, 2024

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

DNA Microarrays: Sample Quality Control, Array Hybridization and Scanning

DNA Microarrays: Sample Quality Control, Array Hybridization and Scanning

Published on: March 15, 2011

Area of Science:

Systems biology
Bioinformatics
Computational biology

Background:

Systems biology research is rapidly expanding, driven by high-throughput data from DNA and protein microarrays.
Analyzing gene expression data from microarrays is crucial for understanding cellular functions and disease states.

Purpose of the Study:

To review the advantages and disadvantages of artificial neural networks (ANN) for microarray data analysis.
To compare ANN performance with alternative methods for classification tasks.

Main Methods:

Review of existing literature on artificial neural networks (ANN) in microarray analysis.
Comparative analysis of ANN against other data analysis techniques.
Discussion of algorithms for combining multiple ANN models.

Main Results:

Artificial neural networks (ANN) show promise for accurate classification of microarray data, including tumor identification.
ANNs can model nonlinear relationships and do not require specific data distribution assumptions.
Algorithms for ensemble ANN methods are presented for improved performance.

Conclusions:

Artificial neural networks (ANN) are a valuable tool for analyzing complex microarray data in systems biology.
Future research may focus on using ANN-processed data for cell and tissue simulations.