SNUH methylation classifier for CNS tumors Article Swipe
Kwang-Hoon Lee
,
Jaemin Jeon
,
Jin Woo Park
,
Suwan Yu
,
Jae‐Kyung Won
,
Kwangsoo Kim
,
Chul‐Kee Park
,
Sung‐Hye Park
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1186/s13148-025-01824-0
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1186/s13148-025-01824-0
Background Methylation profiling of central nervous system (CNS) tumors, pioneered by the German Cancer Research Center, has significantly improved diagnostic accuracy. This study aimed to further enhance the performance of methylation classifiers by leveraging publicly available data and innovative machine-learning techniques. Results Seoul National University Hospital Methylation Classifier (SNUH-MC) addressed data imbalance using the Synthetic Minority Over-sampling Technique (SMOTE) algorithm and incorporated OpenMax within a Multi-Layer Perceptron to prevent labeling errors in low-confidence diagnoses. Compared to two published CNS tumor methylation classification models (DKFZ-MC: Deutsches Krebsforschungszentrum Methylation Classifier v11b4: RandomForest, 767-MC: Multi-Layer Perceptron), our SNUH-MC showed improved performance in F1-score. For ‘Filtered Test Data Set 1,’ the SNUH-MC achieved higher F1-micro (0.932) and F1-macro (0.919) scores compared to DKFZ-MC v11b4 (F1-micro: 0.907, F1-macro: 0.627). We evaluated the performance of three classifiers; SNUH-MC, DKFZ-MC v11b4, and DKFZ-MC v12.5, using specific criteria. We set established ‘Decisions’ categories based on histopathology, clinical information, and next-generation sequencing to assess the classification results. When applied to 193 unknown SNUH methylation data samples, SNUH-MC notably improved diagnosis compared to DKFZ-MC v11b4. Specifically, 17 cases were reclassified as ‘Match’ and 34 cases as ‘Likely Match’ when transitioning from DKFZ-MC v11b4 to SNUH-MC. Additionally, SNUH-MC demonstrated similar results to DKFZ-MC v12.5 for 23 cases that were unclassified by v11b4. Conclusions This study presents SNUH-MC, an innovative methylation-based classification tool that significantly advances the field of neuropathology and bioinformatics. Our classifier incorporates cutting-edge techniques such as the SMOTE and OpenMax resulting in improved diagnostic accuracy and robustness, particularly when dealing with unknown or noisy data.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1186/s13148-025-01824-0
- https://clinicalepigeneticsjournal.biomedcentral.com/counter/pdf/10.1186/s13148-025-01824-0
- OA Status
- gold
- Cited By
- 2
- References
- 42
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4408362364
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4408362364Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1186/s13148-025-01824-0Digital Object Identifier
- Title
-
SNUH methylation classifier for CNS tumorsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
-
2025-03-12Full publication date if available
- Authors
-
Kwang-Hoon Lee, Jaemin Jeon, Jin Woo Park, Suwan Yu, Jae‐Kyung Won, Kwangsoo Kim, Chul‐Kee Park, Sung‐Hye ParkList of authors in order
- Landing page
-
https://doi.org/10.1186/s13148-025-01824-0Publisher landing page
- PDF URL
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https://clinicalepigeneticsjournal.biomedcentral.com/counter/pdf/10.1186/s13148-025-01824-0Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://clinicalepigeneticsjournal.biomedcentral.com/counter/pdf/10.1186/s13148-025-01824-0Direct OA link when available
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Perceptron, Classifier (UML), Methylation, Artificial intelligence, Biology, Computer science, Artificial neural network, Gene, GeneticsTop concepts (fields/topics) attached by OpenAlex
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2Total citation count in OpenAlex
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2025: 2Per-year citation counts (last 5 years)
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42Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.Krebsforschungszentrum | 86 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 95 |
| corresponding_author_ids | https://openalex.org/A5107936772 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 8 |
| corresponding_institution_ids | https://openalex.org/I139264467 |
| citation_normalized_percentile.value | 0.93697863 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |