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Common myeloid progenitors (CMPs) are oligopotent cells that can differentiate into granulocytes and macrophages. Granulocytes and macrophages are essential for protecting the body against bacterial, viral, or fungal infections. They migrate from the bone marrow into the circulating blood to reach specific tissue sites where they differentiate and help in immune surveillance. However, they survive only for a few days and must be continuously made available to the organism to maintain a robust...
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Related Experiment Video

Updated: Dec 4, 2025

Murine Model of Leukemia Relapse to Induction Chemotherapy for Acute Lymphoblastic Leukemia
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Convergent learning-based model for leukemia classification from gene expression.

Pradeep Kumar Mallick1, Saumendra Kumar Mohapatra2, Gyoo-Soo Chae3

  • 1School of Computer Engineering, KIIT (Deemed to be University), Bhubaneswar, Odisha India.

Personal and Ubiquitous Computing
|October 26, 2020
PubMed
Summary
This summary is machine-generated.

Deep learning accurately classifies leukemia subtypes from gene expression data. This automated approach aids in diagnosing gene-related diseases and malfunctions.

Keywords:
ALLAMLDNNDeep learningGene expressionMicroarray

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Machine Learning

Background:

  • Microarray data analysis presents significant challenges due to large data volumes.
  • Automated gene data classification using machine learning is crucial for diagnosing genetic malfunctions and diseases.

Purpose of the Study:

  • To present a deep neural network (DNN) classification method for gene expression data.
  • To evaluate the performance of a five-layer DNN in classifying leukemia subtypes.

Main Methods:

  • Utilized a five-layer deep neural network (DNN) classifier.
  • Trained the DNN with 80% of the leukemia patient bone marrow expression data, with 20% reserved for validation.
  • Assumed non-degenerate inputs, an over-parameterized network, and a sufficient number of hidden neurons.

Main Results:

  • The proposed DNN classifier achieved 98.2% accuracy in classifying acute lymphocyte leukemia (ALL) and acute myelocytic leukemia (AML).
  • Achieved 96.59% sensitivity and 97.9% specificity for leukemia subtype classification.
  • Demonstrated satisfactory performance compared to other classifiers.

Conclusions:

  • Deep neural networks are effective for classifying large-scale gene expression datasets.
  • This computer-aided analysis method shows promise for genetic and virology research.
  • The DNN approach offers a robust tool for accurate leukemia diagnosis.