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Epigenetic Regulation

Epigenetic Regulation

Epigenetic changes alter the physical structure of the DNA without changing the genetic sequence and often regulate whether genes are turned on or off. This regulation ensures that each cell produces only proteins necessary for its function. For example, proteins that promote bone growth are not produced in muscle cells. Epigenetic mechanisms play an essential role in healthy development. Conversely, precisely regulated epigenetic mechanisms are disrupted in diseases like cancer.
X-chromosome...

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Predicting liver cancer on epigenomics data using machine learning.

Vishalkumar Vekariya¹, Kalpdrum Passi¹, Chakresh Kumar Jain²

¹School of Engineering and Computer Science, Laurentian University, Sudbury, ON, Canada.

Frontiers in Bioinformatics

|October 28, 2022

Summary

This summary is machine-generated.

This study uses machine learning to predict liver cancer (LIHC) by analyzing epigenetic modifications. The best model achieved 99.67% accuracy, identifying key gene expression patterns for early detection.

Keywords:

DNA methylation RNA epigenomics histone human genome

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Genome-Wide Analysis of DNA Methylation in Gastrointestinal Cancer

Genome-Wide Analysis of DNA Methylation in Gastrointestinal Cancer

Published on: September 18, 2020

Area of Science:

Genomics and Bioinformatics
Cancer Biology
Computational Biology

Background:

Epigenetic modifications alter gene expression without changing DNA sequence, playing a crucial role in cancer development.
Liver hepatocellular carcinoma (LIHC) is a significant global health issue, causing numerous deaths annually.
Understanding epigenetic alterations in LIHC is vital for improving diagnosis and treatment.

Purpose of the Study:

To develop a machine learning model for predicting liver hepatocellular carcinoma (LIHC) gene expression.
To identify the most effective feature selection and classification methods for LIHC prediction using multi-omics data.
To enhance early detection and understanding of liver cancer through computational analysis.

Main Methods:

Utilized multi-omics data for LIHC, including methylation, histone, human genome, and RNA sequences, accessed via The Cancer Genome Atlas (TCGA).
Applied nine feature selection methods and eight classification algorithms, evaluating performance using 5-fold cross-validation and varying training-to-test ratios.
Focused on 1,000 initial features, refining the selection process to identify the most predictive markers.

Main Results:

The optimal model employed the ReliefF feature selection method, identifying 140 key features.
The XGBoost classification algorithm achieved an Area Under the Curve (AUC) of 1.0 and an accuracy of 99.67% in predicting LIHC.
This demonstrates a highly accurate prediction of liver cancer based on epigenetic and gene expression data.

Conclusions:

Machine learning, particularly using ReliefF and XGBoost, can accurately predict liver hepatocellular carcinoma (LIHC).
The study highlights the potential of integrating multi-omics epigenetic data for robust cancer prediction models.
This approach offers a promising avenue for improving early detection and personalized medicine in liver cancer treatment.