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

How Data are Classified: Numerical Data

How Data are Classified: Numerical Data

Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...

How Data are Classified: Categorical Data

How Data are Classified: Categorical Data

A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...

Sequence Networks of Rotating Machines

Sequence Networks of Rotating Machines

A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...

What is Gene Expression?

What is Gene Expression?

Overview
Gene expression is the process in which DNA directs the synthesis of functional products, that is, proteins. Cells can regulate gene expression at various stages. It allows organisms to generate different cell types and enables cells to adapt to internal and external factors.
Genetic Information Flows from DNA to RNA to Protein
A gene is a stretch of DNA that serves as the blueprint for functional RNAs and proteins. Since DNA is made up of nucleotides and proteins consist of amino...

Vector Representation of Complex Numbers

Vector Representation of Complex Numbers

Complex numbers, represented in Cartesian coordinates, can also be visualized as vectors. These vectors can be expressed in polar form, emphasizing their magnitude and angle. When a complex number is input into a function, the output is another complex number, highlighting the function's zero point from which the vector representation can originate.
Consider a function defined as the product of the complex factors in the numerator divided by the product of the complex factors in the...

mRNA Stability and Gene Expression

mRNA Stability and Gene Expression

The structure and stability of mRNA molecules regulates gene expression, as mRNAs are a key step in the pathway from gene to protein. In eukaryotes, the half-life of mRNA varies from a few minutes up to several days. mRNA stability is essential in growth and development. The absence of the proteins regulating its stability, such as tristetraprolin in mice, can cause systemic issues, including bone marrow overgrowth, inflammation, and autoimmunity.
Cis-acting Elements involved in mRNA stability

You might also read

Related Articles

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

Sort by

Same author

Precision tumor-on-chip for personalized assessment of drug efficacy and immune cell delivery.

Cell reports methods·2026

Same author

Mapping the future of medicine through digital twins.

Frontiers in molecular medicine·2026

Same author

Implementing physics-informed neural networks with deep learning for differential equations.

Frontiers in artificial intelligence·2026

Same author

Functionally Characterizing the Renal Cell Carcinoma Tumor-Immune Microenvironment via Patient-Derived Ex Vivo Models.

Cancer research communications·2026

Same author

Screening for Celiac Disease in Childhood: Cost-Effectiveness of Multiple Genetic and Serological Testing Approaches.

Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association·2025

Same author

The role of digital twins in P4 medicine: A paradigm for modern healthcare.

NPJ digital medicine·2025

Search research articles

Related Experiment Video

Updated: Jan 25, 2026

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

Comparing deep belief networks with support vector machines for classifying gene expression data from complex

Johannes Smolander^1,2, Matthias Dehmer^3,4,5, Frank Emmert-Streib^1,6

¹Predictive Society and Data Analytics Lab, Faculty of Information Technology and Communication Sciences, Tampere University, Finland.

|May 11, 2019

Summary

Deep belief networks (DBNs) show promise for classifying complex genomics data, aiding disease prediction. This study offers guidance on using DBNs and support vector machines (SVMs) for high-dimensional gene expression data analysis.

Keywords:

artificial intelligence deep belief network deep learning genomics neural networks support vector machine

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

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Related Experiment Videos

Last Updated: Jan 25, 2026

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

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

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Area of Science:

Computational biology
Genomics
Machine learning

Background:

Genomics data offers potential for translational research and clinical applications like disease stage prediction.
Classifying high-dimensional, noisy, and heterogeneous genomics data presents significant challenges.
Deep learning methods, while powerful, have seen limited exploration in genomics due to their complexity.

Purpose of the Study:

To investigate the utility of deep belief networks (DBNs) for classifying high-dimensional gene expression data in computational diagnostics.
To compare the performance of DBNs against support vector machines (SVMs) for disease classification.
To explore the effectiveness of combined DBN-SVM classifiers for genomics data analysis.

Main Methods:

Classification of breast cancer and inflammatory bowel disease patients using high-dimensional gene expression data.
Comprehensive analysis of deep belief network (DBN) classification performance across various model parameters.
Comparative study of DBNs versus support vector machines (SVMs).
Investigation of hybrid classifiers integrating DBNs as representation learners for SVMs.

Main Results:

Deep belief networks (DBNs) demonstrate utility in classifying complex gene expression data.
Performance of DBNs was analyzed in detail, considering multiple model parameters.
Combined DBN-SVM classifiers were explored, leveraging DBNs for feature learning input to SVMs.
Comparative analysis provided insights into the strengths of DBNs and SVMs for genomics data.

Conclusions:

Deep belief networks (DBNs) are a viable tool for the complex task of classifying gene expression data in diseases.
The study provides practical guidelines for the application of DBNs in genomics, particularly when combined with SVMs.
Findings contribute to understanding the role of deep learning in computational diagnostics for complex diseases.