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The Detection of 5-Hydroxymethylcytosine in Neural Stem Cells and Brains of Mice

The Detection of 5-Hydroxymethylcytosine in Neural Stem Cells and Brains of Mice

Published on: September 19, 2019

SNUH methylation classifier for CNS tumors.

Kwanghoon Lee¹, Jaemin Jeon², Jin Woo Park³

¹Department of Pathology, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, Republic of Korea.

Clinical Epigenetics

|March 13, 2025

Summary

This study introduces the Seoul National University Hospital Methylation Classifier (SNUH-MC), an advanced tool for diagnosing central nervous system (CNS) tumors. SNUH-MC improves diagnostic accuracy by using machine learning techniques like SMOTE and OpenMax, outperforming previous methylation classification models.

Keywords:

Brain tumors Classification Methylation Next-generation sequencing Targeted therapy

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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:

Neuropathology
Bioinformatics
Computational Biology

Background:

Methylation profiling is crucial for diagnosing central nervous system (CNS) tumors.
Previous methods, like the German Cancer Research Center's classifier, have improved diagnostic accuracy.
There is a need for enhanced methylation classifiers using advanced machine learning.

Purpose of the Study:

To develop an improved methylation classifier for CNS tumors.
To leverage publicly available data and machine learning for enhanced diagnostic performance.
To address data imbalance and prevent misclassification in low-confidence diagnoses.

Main Methods:

Developed the Seoul National University Hospital Methylation Classifier (SNUH-MC).
Employed the Synthetic Minority Over-sampling Technique (SMOTE) to handle data imbalance.
Integrated OpenMax with a Multi-Layer Perceptron to reduce labeling errors.

Main Results:

SNUH-MC demonstrated superior performance with higher F1-micro (0.932) and F1-macro (0.919) scores compared to DKFZ-MC v11b4.
Reclassified 17 cases as 'Match' and 34 as 'Likely Match' when applied to unknown SNUH methylation data.
Achieved comparable results to DKFZ-MC v12.5 for cases previously unclassified by v11b4.

Conclusions:

SNUH-MC is an innovative methylation-based classification tool for neuropathology and bioinformatics.
The classifier enhances diagnostic accuracy and robustness, especially with noisy or unknown data.
Utilizes advanced techniques like SMOTE and OpenMax for improved performance.