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DNA Microarrays

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...

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

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Diagnosis of leukemia using microarray analysis based on Hidden Markov Model and Random Convolutional Kernel

Sareh Baqeri Matak¹, Elham Askari², Sara Motamed²

¹Department of Computer Engineering, Rasht Branch, Islamic Azad University, Rasht, Iran.

Computational Biology and Chemistry

|September 14, 2025

Summary

This study uses deep learning and microarray data to accurately detect leukemia, achieving 99.26% accuracy. Identifying DNA alterations aids in early leukemia diagnosis and intervention.

Keywords:

Gene Hidden Markov Model Leukemia Microarray Random Convolutional Kernel Transformation

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A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

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Published on: October 25, 2018

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Leukemia is a prevalent cancer where early detection is crucial.
Microarray data analysis for leukemia diagnosis is complex due to the large number of genes.
Identifying key genes is vital for accurate disease diagnosis.

Purpose of the Study:

To enhance leukemia type diagnostic accuracy using microarray data and deep learning.
To develop a model for selecting essential diagnostic features and sequences.
To predict five leukemia categories from sample data.

Main Methods:

Feature selection and sequence processing using Generative Adversarial Network (GAN) with U-Net architecture for synthetic data generation.
Data labeling, feature ranking via Hidden Markov Model (HMM), and classification using Random Convolutional Kernel Transformation (ROCKET).
Integration of original and synthetic data for comprehensive analysis.

Main Results:

The proposed deep learning model achieved a high classification accuracy of 99.26%.
The model demonstrated superior performance compared to existing diagnostic methods.
Successful prediction of five distinct leukemia categories was achieved.

Conclusions:

Leveraging DNA alterations and genetic mutations significantly improves leukemia diagnostics.
Identifying genomic modifications aids in predicting leukemia risk and facilitates early detection.
The study underscores the potential of advanced computational methods for timely leukemia intervention.