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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...

Epigenetic Regulation

Epigenetic Regulation

Epigenetic mechanisms play an essential role in healthy development. Conversely, precisely regulated epigenetic mechanisms are disrupted in diseases like cancer.

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Updated: Mar 14, 2026

Methyl-binding DNA capture Sequencing for Patient Tissues

Methyl-binding DNA capture Sequencing for Patient Tissues

Published on: October 31, 2016

Identifying Significant Features in Cancer Methylation Data Using Gene Pathway Segmentation.

Zena M Hira¹, Duncan F Gillies¹

¹Department of Computing, Imperial College London, London, UK.

Cancer Informatics

|October 1, 2016

Summary

This summary is machine-generated.

This study introduces a novel machine learning approach for cancer diagnostics. By segmenting methylation data into gene pathways, it enables faster identification of predictive biomarkers for effective cancer therapy.

Keywords:

cancer progression machine learning methylation profiling

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Methyl-binding DNA capture Sequencing for Patient Tissues

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Published on: October 31, 2016

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Published on: June 16, 2017

Area of Science:

Genomics and Bioinformatics
Computational Biology
Cancer Research

Background:

Predicting patient response to cancer therapy is crucial for effective treatment.
Current biomarker discovery relies on hypothesis testing, which is computationally challenging with large microarray datasets.
Machine learning offers multivariate analysis but faces scalability issues with increasing data size.

Purpose of the Study:

To develop a computationally feasible method for identifying predictive gene sets from methylation data.
To improve the accuracy and efficiency of cancer biomarker discovery.
To leverage prior biological knowledge within a machine learning framework.

Main Methods:

Segmenting methylation microarray datasets into subsets based on known gene pathways.
Utilizing the AdaBoost algorithm with decision trees for classification within each pathway subset.
Independently classifying and assessing the diagnostic value of each pathway subset.

Main Results:

The pathway-based segmentation significantly reduces computational complexity for machine learning.
Individual pathway subsets can be rapidly trained and tested for diagnostic information.
Combining genes from successful pathways yields a highly accurate predictive classifier.

Conclusions:

This approach effectively integrates prior biological knowledge with machine learning for biomarker discovery.
It offers a scalable and efficient method for identifying predictive gene sets in cancer methylation data.
The developed method has the potential to enhance personalized cancer treatment strategies.