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

Artificial Intelligence in Non-Alcoholic Fatty Liver Disease and Fibrosis: A Narrative Review.

Current pharmaceutical design·2026
Same author

Association Between Salivary and Cervical Human Papillomavirus in Women With a History of Oral Sex.

Journal of lower genital tract disease·2026
Same author

Dynamic evaluation of the glycolytic determinants LDH-A and GLUT-1 enhances prognostic significance and their inhibition affects the growth of mesothelioma spheroids.

Molecular metabolism·2026
Same author

The therapeutic potential of <i>Ziziphus jujuba</i> in colorectal cancer: An <i>in-vitro</i> study.

Avicenna journal of phytomedicine·2026
Same author

Retraction Note: Targeting transforming growth factor beta (TGF-β) using Pirfenidone, a potential repurposing therapeutic strategy in colorectal cancer.

Scientific reports·2026
Same author

Novel FAK inhibitors suppress tumor growth and reverse EGFR-TKI resistance in non-small cell lung cancer.

Cancer drug resistance (Alhambra, Calif.)·2025

Related Experiment Video

Updated: Dec 5, 2025

Flow Cytometry to Estimate Leukemia Stem Cells in Primary Acute Myeloid Leukemia and in Patient-derived-xenografts, at Diagnosis and Follow Up
09:01

Flow Cytometry to Estimate Leukemia Stem Cells in Primary Acute Myeloid Leukemia and in Patient-derived-xenografts, at Diagnosis and Follow Up

Published on: March 26, 2018

14.4K

Deep Learning for Acute Myeloid Leukemia Diagnosis.

Elham Nazari1, Amir Hossein Farzin2, Mehran Aghemiri3

  • 1Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.

Journal of Medicine and Life
|October 19, 2020
PubMed
Summary

Deep learning significantly improves acute myeloid leukemia diagnosis using DNA microarray data. This advanced technique achieves 96.67% accuracy, outperforming traditional methods for cancer screening.

Keywords:
AMLdeep learningmachine learningmicroarrayneural network

More Related Videos

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

7.2K
Two Flow Cytometric Approaches of NKG2D Ligand Surface Detection to Distinguish Stem Cells from Bulk Subpopulations in Acute Myeloid Leukemia
05:24

Two Flow Cytometric Approaches of NKG2D Ligand Surface Detection to Distinguish Stem Cells from Bulk Subpopulations in Acute Myeloid Leukemia

Published on: February 21, 2021

4.5K

Related Experiment Videos

Last Updated: Dec 5, 2025

Flow Cytometry to Estimate Leukemia Stem Cells in Primary Acute Myeloid Leukemia and in Patient-derived-xenografts, at Diagnosis and Follow Up
09:01

Flow Cytometry to Estimate Leukemia Stem Cells in Primary Acute Myeloid Leukemia and in Patient-derived-xenografts, at Diagnosis and Follow Up

Published on: March 26, 2018

14.4K
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

7.2K
Two Flow Cytometric Approaches of NKG2D Ligand Surface Detection to Distinguish Stem Cells from Bulk Subpopulations in Acute Myeloid Leukemia
05:24

Two Flow Cytometric Approaches of NKG2D Ligand Surface Detection to Distinguish Stem Cells from Bulk Subpopulations in Acute Myeloid Leukemia

Published on: February 21, 2021

4.5K

Area of Science:

  • Oncology
  • Bioinformatics
  • Computational Biology

Background:

  • Increasing cancer incidence necessitates accurate diagnostic tools.
  • DNA microarrays measure gene expression for cancer diagnosis and screening.
  • High-dimensional gene expression data challenges traditional statistical analysis.

Purpose of the Study:

  • To apply novel techniques for diagnosing acute myeloid leukemia (AML).
  • To evaluate the efficacy of deep learning models in AML diagnosis using microarray data.

Main Methods:

  • Extracted 22,283 genes of leukemia microarray data from the Gene Expression Omnibus repository.
  • Performed initial data preprocessing including normalization tests and Principal Component Analysis (PCA) in Python.
  • Designed and implemented Deep Neural Networks (DNNs) and validated results with classifiers.

Main Results:

  • Normalization test was significant (P>0.05).
  • PCA demonstrated gene segregation potential, distinguishing cancer and healthy cells.
  • DNNs achieved 96.67% accuracy, significantly higher than single-layer neural networks (63.33%).

Conclusions:

  • Deep learning methods enhance diagnostic accuracy and performance in cancer detection.
  • The study recommends employing deep learning for improved cancer diagnosis and gene selection.
  • This approach shows promise for various cancer types beyond AML.