Search research articles

Related Concept Videos

Synthetic Biology

Synthetic Biology

Synthetic biology is an interdisciplinary science that involves using principles from disciplines such as engineering, molecular biology, cell biology, and systems biology. It involves remodeling existing organisms from nature or constructing completely new synthetic organisms for applications such as protein or enzyme production, bioremediation, value-added macromolecule production, and the addition of desirable traits to crops, to name a few.
Golden rice
Golden rice is a genetically modified...

DNA as a Genetic Template

DNA as a Genetic Template

Protein Networks

Protein Networks

Gene Duplication and Divergence

Gene Duplication and Divergence

The seminal work of Ohno in 1970 popularized the idea of gene duplication and divergence. DNA sequence comparison studies reveal that a large portion of the genes in bacteria, archaebacteria, and eukaryotes was generated by gene duplication and divergence, indicating its critical role in evolution.
The duplicated copies of the gene are called Paralogs. Paralogs with similar sequences and functions form a gene family. Across several species, a large number of gene families are...

Combinatorial Gene Control

Combinatorial Gene Control

Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
The expression of more than 30,000 genes is controlled by approximately 2000-3000 transcription factors. This is possible because a single transcription factor can recognize more than one regulatory sequence. The specificity in gene...

You might also read

Related Articles

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

Sort by

Same author

Photoacoustic signal to image based convolutional neural network for defect detection.

The Review of scientific instruments·2025

Same author

Emotion recognition using time-frequency ridges of EEG signals based on multivariate synchrosqueezing transform.

Biomedizinische Technik. Biomedical engineering·2021

Same author

Breast Cancer Detection with Reduced Feature Set.

Computational and mathematical methods in medicine·2015

Same journal

Multi-Scale convolutional neural networks integrated with self-attention for motor imagery EEG decoding.

Biomedical engineering letters·2026

Same journal

Low-power analog and mixed-signal circuit techniques for next-generation miniature implantable neural interface systems.

Biomedical engineering letters·2026

Same journal

Advances in semiconductor materials and device architectures for biomedical systems: a mini review.

Biomedical engineering letters·2026

Same journal

A Multi-perception fusion using shared-control method for brain-mobile robot.

Biomedical engineering letters·2026

Same journal

SSA-DCNet: a cross-session MI-EEG classification network based on deformable convolution and spatial-shift attention.

Biomedical engineering letters·2026

Same journal

Advanced silicon nanomembrane based bioelectronics for flexible and stretchable implantable systems.

Biomedical engineering letters·2026

See all related 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

Search research articles

Related Experiment Video

Updated: Aug 12, 2025

Using Generative Art to Convey Past and Future Climate Transitions

Using Generative Art to Convey Past and Future Climate Transitions

Published on: March 31, 2023

Enhanced dataset synthesis using conditional generative adversarial networks.

¹Department of Mechatronics Engineering, Bursa Technical University, 16330 Yildirim, Bursa, Turkey.

Biomedical Engineering Letters

|January 30, 2023

Summary

This summary is machine-generated.

This study introduces a Conditional Generative Adversarial Network (CGAN) to create synthetic biomedical datasets, improving computer-aided diagnosis (CAD) accuracy and reducing data collection efforts.

Keywords:

Conditional GAN Dataset synthesis Deep learning Feature extraction Generative adversarial network

More Related Videos

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Published on: March 18, 2019

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

Related Experiment Videos

Last Updated: Aug 12, 2025

Using Generative Art to Convey Past and Future Climate Transitions

Using Generative Art to Convey Past and Future Climate Transitions

Published on: March 31, 2023

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Published on: March 18, 2019

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

Area of Science:

Biomedical data science
Machine learning in healthcare
Medical imaging analysis

Background:

Acquiring sufficient biomedical data for training is challenging and resource-intensive.
Computer-aided diagnosis (CAD) algorithm performance is highly dependent on sample size and feature space.
Limited datasets can hinder the development and accuracy of diagnostic tools.

Purpose of the Study:

To propose a Conditional Generative Adversarial Network (CGAN) for enhanced feature generation and synthetic dataset creation.
To address the difficulties in biomedical data acquisition and improve the success rates of CAD algorithms.
To generate synthetic datasets with high class separability, enabling larger sample sizes for model training.

Main Methods:

Utilized a Conditional Generative Adversarial Network (CGAN) to synthesize enhanced feature vectors and create large sample datasets.
Trained the CGAN model using 25% of five diverse medical datasets.
Evaluated and compared the performance of synthetic datasets against original data across various sample sizes.

Main Results:

The proposed CGAN model successfully generated synthetic datasets with enhanced class separability.
Generated datasets demonstrated the potential to increase the accuracy rates of CAD systems.
Physicians can benefit from reduced sample collection efforts through CGAN-based data synthesis.

Conclusions:

Conditional Generative Adversarial Networks offer a viable solution for augmenting limited biomedical datasets.
The CGAN approach enhances feature vectors, leading to improved CAD system performance.
This method significantly reduces the time and effort required for participant recruitment and data collection in medical research.