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

Evolutionary Relationships through Genome Comparisons

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...

You might also read

Related Articles

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

Sort by

Same author

Retraction Note: Analyzing fibrous tissue pattern in fibrous dysplasia bone images using deep R-CNN networks for segmentation.

Soft computing·2026

Same author

A novel 4D-hybrid chaotic system and concentric rings based permutation-diffusion approach to encrypt traffic images.

Scientific reports·2026

Same author

Drug-resistant abdominal tuberculosis in children: dilemmas and difficulties.

Infectious diseases (London, England)·2026

Same author

Leveraging neural networks to model tourist behaviour for sustainable heritage conservation.

Scientific reports·2026

Same author

Early Knee Osteoarthritis Detection by Multi-Component T<sub>2</sub> Mapping.

Bioengineering (Basel, Switzerland)·2026

Same author

Analysis of pressure flotation mechanisms and their practical application in the treatment of metal-containing wastewater.

Scientific reports·2026

Same journal

AdaWGAN: Data Augmentation for Few-Shot HD-sEMG Gesture Recognition Using Single-Trial Data.

IEEE journal of biomedical and health informatics·2026

Same journal

NeuroBooster: a domain-informed self-supervised learning paradigm tailored for brain MRI analysis.

IEEE journal of biomedical and health informatics·2026

Same journal

Graph Convolutional Neural Network based Depression Detection using Brain Functional Connectivity Measures.

IEEE journal of biomedical and health informatics·2026

Same journal

Improving Multi-Sensor Non-Invasive Glucose Detection through AI: A Domain Generalization Approach.

IEEE journal of biomedical and health informatics·2026

Same journal

Unmixing the Neck: Accurate Jugular Venous Pulse Detection From Wearable PPG.

IEEE journal of biomedical and health informatics·2026

Same journal

AD-DAE: Alzheimer's Disease Progression Modeling with Unpaired Longitudinal MRI using Diffusion Auto-Encoders.

IEEE journal of biomedical and health informatics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 30, 2025

High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method

High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method

Published on: May 21, 2018

Deep Drug Synergy Prediction Network Using Modified Triangular Mutation-Based Differential Evolution.

Dilbag Singh, Ahmad Ali Alzubi, Manjit Kaur

IEEE Journal of Biomedical and Health Informatics

|March 18, 2024

Summary

This summary is machine-generated.

Predicting drug synergy for cancer treatment is challenging. A new deep learning model, EDNet, uses a novel algorithm to improve prediction accuracy and enhance combination therapy effectiveness.

More Related Videos

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

Related Experiment Videos

Last Updated: Jun 30, 2025

High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method

High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method

Published on: May 21, 2018

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

Area of Science:

Computational biology
Pharmacology
Artificial intelligence in medicine

Background:

Drug combination therapy is vital for cancer treatment, but predicting synergistic effects is complex.
Existing machine learning and deep learning models face challenges like gradient vanishing and overfitting.

Purpose of the Study:

To propose EDNet, a deep drug synergy prediction network.
To address limitations of current models using a modified differential evolution algorithm.

Main Methods:

EDNet employs a deep bidirectional mixture density network.
A modified triangular mutation-based differential evolution algorithm optimizes network weights and architecture.
The model automatically extracts features and provides conditional probability distributions.

Main Results:

EDNet demonstrated superior performance on NCI-ALMANAC and deep-synergy datasets.
The proposed algorithm effectively evolved network attributes, improving synergy prediction.
EDNet outperformed existing competing models in drug synergy prediction.

Conclusions:

EDNet offers an effective solution for predicting drug synergy in cancer treatment.
The model enhances the efficiency and effectiveness of drug combinations.
This facilitates improved cancer treatment outcomes through better drug interactions.