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Updated: Jun 30, 2025

Genome-Wide Analysis of DNA Methylation in Gastrointestinal Cancer

Genome-Wide Analysis of DNA Methylation in Gastrointestinal Cancer

Published on: September 18, 2020

An Intelligent Classification System for Cancer Detection Based on DNA Methylation Using ML and Semantic Knowledge in

Anuradha Thakare¹, Manisha Bhende², Mulugeta Tesema³

¹Department of Computer Engineering, Pimpri Chinchwad College of Engineering, Pune, India.

Computational Intelligence and Neuroscience

|March 19, 2024

Summary

This summary is machine-generated.

This study introduces a novel machine learning approach using wearable sensors and DNA methylation data for accurate cancer classification. The method effectively addresses imbalanced datasets and improves detection sensitivity for minority classes.

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Genome-Wide Analysis of DNA Methylation in Gastrointestinal Cancer

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Methyl-binding DNA capture Sequencing for Patient Tissues

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Published on: October 31, 2016

Area of Science:

Biomedical Engineering
Computational Biology
Machine Learning in Healthcare

Background:

Wearable sensing devices are increasingly used for assessing patient wellness and diagnosing chronic conditions.
Social network analysis (SNA) and machine learning (ML) are emerging tools in healthcare for analyzing complex data.
DNA methylation, an epigenetic process, is linked to various health issues, including cancer.

Purpose of the Study:

To develop an intelligent system using wearable sensors for cancer classification based on DNA methylation data.
To address challenges of class imbalance and high dimensionality in large biomedical datasets like The Cancer Genome Atlas (TCGA).
To enhance the accuracy and sensitivity of cancer detection, particularly for minority classes.

Main Methods:

A mixed-sampling imbalanced data ensemble classification technique was developed, incorporating Intelligent Synthetic Minority Oversampling (SMOTE) and Tomek Link methods.
The system utilizes wearable biomedical sensors to collect DNA methylation data.
A deep forest (GC-Forest) algorithm with cascading forest structures was employed for final classification.

Main Results:

The proposed technique effectively handles class imbalance and noise in large datasets.
The method demonstrated increased sensitivity for the minority class in cancer classification.
Classification accuracy for the majority class was maintained while improving overall detection capabilities.

Conclusions:

The developed mixed-sampling imbalanced data ensemble classification technique shows promise for accurate cancer detection using DNA methylation data from wearable sensors.
This approach offers a viable solution for improving diagnostic accuracy in complex, imbalanced biomedical datasets.
The study highlights the potential of integrating wearable technology, epigenetic data, and advanced machine learning for personalized cancer diagnostics.