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

Combination Therapies and Personalized Medicine

Combination Therapies and Personalized Medicine

Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...

You might also read

Related Articles

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

Sort by

Same author

Gastrointestinal Parasitism in Cattle and Water Buffaloes in Pakistan: Prevalence and Associated Risk Factors.

Acta parasitologica·2026

Same author

Correction: Green synthesis and characterizations of zinc oxide nanoparticles using acorn fruit extract for antimicrobial, larvicidal and in silico activities.

Scientific reports·2026

Same author

The Use of Muscle-Hitch and Internal Gradual Tension Quilting Sutures for Eyebrow Lift Using an Open Temple Approach.

Facial plastic surgery : FPS·2026

Same author

A process-guided uncertainty-aware deep learning framework for reliable and interpretable industrial fault diagnosis.

PloS one·2026

Same author

Removal of iodine from organic media using diethylene triamine-grafted vinylbenzyl chloride-divinylbenzene resin.

RSC advances·2026

Same author

Ecotoxicological insights into fluoride pollution affecting soil, plant and human health.

Ecotoxicology and environmental safety·2026

Search research articles

Related Experiment Video

Updated: Jul 3, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

A machine learning and deep learning-based integrated multi-omics technique for leukemia prediction.

Erum Yousef Abbasi¹, Zhongliang Deng¹, Qasim Ali²

¹State Key Laboratory of Wireless Network Positioning and Communication Engineering Integration Research, School of Electronics Engineering, Beijing University of Posts and Telecommunications, Beijing, China.

|February 14, 2024

Summary

This summary is machine-generated.

This study introduces a novel approach for leukemia diagnosis using machine learning (ML) and deep learning (DL) on multi-omics data. Deep learning, specifically Recurrent Neural Networks (RNNs), achieved 98% accuracy, outperforming ML methods for blood cancer prediction.

Keywords:

Deep learning Genomics Leukemia Machine learning Multi-omics

More Related Videos

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

Author Spotlight: Unveiling Transmembrane Protein Family-Related Markers in Gastric Cancer and Implications for Targeted Therapies

Author Spotlight: Unveiling Transmembrane Protein Family-Related Markers in Gastric Cancer and Implications for Targeted Therapies

Published on: September 15, 2023

Related Experiment Videos

Last Updated: Jul 3, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

Author Spotlight: Unveiling Transmembrane Protein Family-Related Markers in Gastric Cancer and Implications for Targeted Therapies

Author Spotlight: Unveiling Transmembrane Protein Family-Related Markers in Gastric Cancer and Implications for Targeted Therapies

Published on: September 15, 2023

Area of Science:

Oncology
Bioinformatics
Artificial Intelligence

Background:

Expanding cancer data offers opportunities for better understanding and personalized treatment.
Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), is crucial for analyzing multi-omics data in predicting blood cancers like leukemia.
Novel approaches are needed to effectively process and interpret large volumes of biological data for improved diagnostics.

Purpose of the Study:

To introduce and evaluate a novel approach for leukemia diagnosis using integrated multi-omics data.
To compare the diagnostic accuracy of various ML and DL algorithms for leukemia prediction.
To identify the optimal ML or DL technique for accurate and efficient leukemia diagnosis.

Main Methods:

Analysis of integrated multi-omics data using various ML algorithms: Random Forest (RF), Naive Bayes (NB), Decision Tree (DT), Logistic Regression (LR), and Gradient Boosting (GB).
Comparison of ML techniques with DL methods: Recurrent Neural Networks (RNN) and Feedforward Neural Networks (FNN).
Validation based on 17 features including patient demographics, mutation types, and chromosomal data.

Main Results:

Gradient Boosting (GB) achieved 97% accuracy among the ML techniques evaluated.
Recurrent Neural Networks (RNN) demonstrated superior performance with 98% accuracy within the DL methods.
The proposed approach effectively filters unclassified data, highlighting the efficacy of DL for leukemia prediction.

Conclusions:

Deep Learning (DL) methods, particularly RNNs, show significant promise for accurate leukemia prediction using multi-omics data.
The study validates the effectiveness of high-throughput technologies and advanced algorithms in improving healthcare diagnostics and patient care.
Comparing ML and DL techniques identified DL as the superior approach for optimizing leukemia diagnosis accuracy.