Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Enhancing aqueous solubility prediction with residual gated graph convolutions and sequential modeling.

Computational biology and chemistry·2026
Same author

Synthetic Expansion of Blood Dielectric Spectra at Microwave Frequencies Using Data-Driven Methods.

Sensors (Basel, Switzerland)·2026
Same author

Corrigendum to "LLM predicts human behavior: A BERT-based approach for conscientiousness personality trait detection from online content" [Acta Psychologica 266 (2026), 106832].

Acta psychologica·2026
Same author

Tuning the electronic and photovoltaic properties of naphthalene diamine through molecular engineering with efficient acceptors: a quantum chemical study.

Scientific reports·2026
Same author

Monogenic kidney disease and monogenic diabetes are present in renal clinic patients with non-genetic diagnoses.

Scientific reports·2026
Same author

LLM predicts human behavior: A BERT-based approach for conscientiousness personality trait detection from online content.

Acta psychologica·2026
Same journal

Characterization of genomic diversity in bacteriophages infecting Rhodococcus.

PloS one·2026
Same journal

Effectiveness of the Responding to Experienced and Anticipated Discrimination (READ) training on reducing stigma for medical students in Tunisia.

PloS one·2026
Same journal

Cell-cell junction gene signatures as subtype-specific prognostic biomarkers in breast cancer.

PloS one·2026
Same journal

GC-MS based tentative identification of γ-sitosterol from Brassica nigra seeds and evaluation of its anticancer potential: An integrated in vitro and in silico study.

PloS one·2026
Same journal

Ad-based social media interventions increase belief accuracy and generate pro-social opinions among non-news readers.

PloS one·2026
Same journal

Negotiating knowledge: The role of network hedging in the production of high-impact science.

PloS one·2026
See all related articles

Related Experiment Video

Updated: May 3, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.0K

An efficient leukemia prediction method using machine learning and deep learning with selected features.

Mahwish Ilyas1, Muhammad Ramzan2, Mohamed Deriche3

  • 1Department of Computer Science, The University of Lahore, Sargodha Campus, Sargodha, Punjab, Pakistan.

Plos One
|May 16, 2025
PubMed
Summary
This summary is machine-generated.

Accurate leukemia subtype classification is crucial for effective treatment. This study successfully used machine and deep learning on gene data, achieving 100% accuracy with LSTM for precise leukemia identification.

More Related Videos

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.0K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

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

Published on: August 16, 2020

5.8K

Related Experiment Videos

Last Updated: May 3, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.0K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.0K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

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

Published on: August 16, 2020

5.8K

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Leukemia, a life-threatening blood cancer, necessitates early and accurate diagnosis for improved patient outcomes.
  • Current diagnostic methods, including manual microscopic examination, face challenges in identifying specific leukemia subtypes crucial for targeted therapies.
  • The Curated Microarray Database (CuMiDa) provides valuable gene expression data for leukemia research.

Purpose of the Study:

  • To predict and classify leukemia subtypes using gene expression data from the CuMiDa database (GSE9476).
  • To evaluate the efficacy of machine learning (ML) and deep learning techniques in classifying leukemia subtypes based on selected genetic features.
  • To identify the most differentiating features for accurate leukemia subtype classification.

Main Methods:

  • Feature selection was applied to identify the 25 most differentiating genes from a dataset of 22,283 genes in 64 CuMiDa samples.
  • Machine learning algorithms including Random Forest, Linear Regression, and Support Vector Machines (SVM) were employed for classification.
  • Deep learning techniques, specifically Long Short-Term Memory (LSTM) networks, were utilized for classification.

Main Results:

  • The study achieved high classification accuracies for leukemia subtypes.
  • Random Forest and SVM demonstrated 96.15% accuracy, while Linear Regression achieved 92.30% accuracy.
  • Long Short-Term Memory (LSTM) networks reached a perfect classification accuracy of 100%.

Conclusions:

  • Deep learning methods, particularly LSTM, significantly outperform traditional ML methods in classifying leukemia subtypes using selected gene features.
  • The proposed approach using feature selection and advanced ML/deep learning techniques offers a promising avenue for accurate and efficient leukemia diagnosis.
  • Accurate subtype classification is vital for tailoring specialized treatment strategies and improving survival rates for leukemia patients.