crossNN: an explainable framework for cross-platform DNA methylation-based classification of cancer
Abstract DNA methylation-based classification of brain tumors has emerged as a powerful and indispensable diagnostic technique. Initial implementations have used methylation microarrays for data generation, but different sequencing approaches are increasingly used. Most current classifiers, however, rely on a fixed methylation feature space, rendering them incompatible with other platforms, especially different flavors of DNA sequencing. Here, we describe crossNN, a neural network-based machine learning framework which can accurately classify tumor entities using DNA methylation profiles obtained from different platforms and with different epigenome coverage and sequencing depth. It outperforms other deep- and shallow machine learning models with respect to precision as well as simplicity and computational requirements while still being fully explainable. Validation in a large cohort of >1,900 tumors profiled using different microarray and sequencing platforms, including low-pass nanopore and targeted bisulfite sequencing, demonstrates the robustness and scalability of the model..
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Preprint| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
bioRxiv.org - (2024) vom: 27. Jan. Zur Gesamtaufnahme - year:2024 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
Yuan, Dongsheng [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| doi: |
10.1101/2024.01.22.24301523 |
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| PPN (Katalog-ID): |
XBI042271738 |
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| 520 | |a Abstract DNA methylation-based classification of brain tumors has emerged as a powerful and indispensable diagnostic technique. Initial implementations have used methylation microarrays for data generation, but different sequencing approaches are increasingly used. Most current classifiers, however, rely on a fixed methylation feature space, rendering them incompatible with other platforms, especially different flavors of DNA sequencing. Here, we describe crossNN, a neural network-based machine learning framework which can accurately classify tumor entities using DNA methylation profiles obtained from different platforms and with different epigenome coverage and sequencing depth. It outperforms other deep- and shallow machine learning models with respect to precision as well as simplicity and computational requirements while still being fully explainable. Validation in a large cohort of >1,900 tumors profiled using different microarray and sequencing platforms, including low-pass nanopore and targeted bisulfite sequencing, demonstrates the robustness and scalability of the model. | ||
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| 700 | 1 | |a Jugas, Robin |4 aut | |
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| 700 | 1 | |a Sterba, Jaroslav |4 aut | |
| 700 | 1 | |a Slaby, Ondrej |4 aut | |
| 700 | 1 | |a Schmid, Simone |4 aut | |
| 700 | 1 | |a Siewert, Christin |4 aut | |
| 700 | 1 | |a Osberg, Brendan |4 aut | |
| 700 | 1 | |a Capper, David |4 aut | |
| 700 | 1 | |a Zeiner, Pia |4 aut | |
| 700 | 1 | |a Weber, Katharina |4 aut | |
| 700 | 1 | |a Harter, Patrick |4 aut | |
| 700 | 1 | |a Jabareen, Nabil |4 aut | |
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| 700 | 1 | |a Euskirchen, Philipp |0 (orcid)0000-0002-9138-805X |4 aut | |
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