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Verfasst von:Hench, Jürgen Christian Hans [VerfasserIn]   i
 Hultschig, Claus [VerfasserIn]   i
 Brugger, Jon [VerfasserIn]   i
 Mariani, Luigi [VerfasserIn]   i
 Guzman, Raphael [VerfasserIn]   i
 Soleman, Jehuda [VerfasserIn]   i
 Leu, Severina [VerfasserIn]   i
 Benton, Miles [VerfasserIn]   i
 Stec, Irenäus Maria [VerfasserIn]   i
 Hench, Ivana Bratic [VerfasserIn]   i
 Hoffmann, Per [VerfasserIn]   i
 Harter, Patrick [VerfasserIn]   i
 Weber, Katharina J. [VerfasserIn]   i
 Albers, Anne [VerfasserIn]   i
 Thomas, Christian [VerfasserIn]   i
 Hasselblatt, Martin [VerfasserIn]   i
 Schüller, Ulrich [VerfasserIn]   i
 Restelli, Lisa [VerfasserIn]   i
 Capper, David [VerfasserIn]   i
 Hewer, Ekkehard [VerfasserIn]   i
 Diebold, Joachim [VerfasserIn]   i
 Kolenc, Danijela [VerfasserIn]   i
 Schneider, Ulf C. [VerfasserIn]   i
 Rushing, Elisabeth [VerfasserIn]   i
 Della Monica, Rosa [VerfasserIn]   i
 Chiariotti, Lorenzo [VerfasserIn]   i
 Sill, Martin [VerfasserIn]   i
 Schrimpf, Daniel [VerfasserIn]   i
 Deimling, Andreas von [VerfasserIn]   i
 Sahm, Felix [VerfasserIn]   i
 Kölsche, Christian [VerfasserIn]   i
 Tolnay, Markus [VerfasserIn]   i
 Frank, Stephan [VerfasserIn]   i
Titel:EpiDiP/NanoDiP
Titelzusatz:a versatile unsupervised machine learning edge computing platform for epigenomic tumour diagnostics
Verf.angabe:Jürgen Hench, Claus Hultschig, Jon Brugger, Luigi Mariani, Raphael Guzman, Jehuda Soleman, Severina Leu, Miles Benton, Irenäus Maria Stec, Ivana Bratic Hench, Per Hoffmann, Patrick Harter, Katharina J. Weber, Anne Albers, Christian Thomas, Martin Hasselblatt, Ulrich Schüller, Lisa Restelli, David Capper, Ekkehard Hewer, Joachim Diebold, Danijela Kolenc, Ulf C. Schneider, Elisabeth Rushing, Rosa della Monica, Lorenzo Chiariotti, Martin Sill, Daniel Schrimpf, Andreas von Deimling, Felix Sahm, Christian Kölsche, Markus Tolnay, Stephan Frank
Jahr:2024
Umfang:16 S.
Illustrationen:Illustrationen
Fussnoten:Online veröffentlicht: 4. April 2024 ; Gesehen am 30.10.2024
Titel Quelle:Enthalten in: Acta Neuropathologica Communications
Ort Quelle:London : Biomed Central, 2013
Jahr Quelle:2024
Band/Heft Quelle:12(2024), Artikel-ID 51, Seite [1]-16
ISSN Quelle:2051-5960
Abstract:DNA methylation analysis based on supervised machine learning algorithms with static reference data, allowing diagnostic tumour typing with unprecedented precision, has quickly become a new standard of care. Whereas genome-wide diagnostic methylation profiling is mostly performed on microarrays, an increasing number of institutions additionally employ nanopore sequencing as a faster alternative. In addition, methylation-specific parallel sequencing can generate methylation and genomic copy number data. Given these diverse approaches to methylation profiling, to date, there is no single tool that allows (1) classification and interpretation of microarray, nanopore and parallel sequencing data, (2) direct control of nanopore sequencers, and (3) the integration of microarray-based methylation reference data. Furthermore, no software capable of entirely running in routine diagnostic laboratory environments lacking high-performance computing and network infrastructure exists. To overcome these shortcomings, we present EpiDiP/NanoDiP as an open-source DNA methylation and copy number profiling suite, which has been benchmarked against an established supervised machine learning approach using in-house routine diagnostics data obtained between 2019 and 2021. Running locally on portable, cost- and energy-saving system-on-chip as well as gpGPU-augmented edge computing devices, NanoDiP works in offline mode, ensuring data privacy. It does not require the rigid training data annotation of supervised approaches. Furthermore, NanoDiP is the core of our public, free-of-charge EpiDiP web service which enables comparative methylation data analysis against an extensive reference data collection. We envision this versatile platform as a useful resource not only for neuropathologists and surgical pathologists but also for the tumour epigenetics research community. In daily diagnostic routine, analysis of native, unfixed biopsies by NanoDiP delivers molecular tumour classification in an intraoperative time frame.
DOI:doi:10.1186/s40478-024-01759-2
URL:Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.

kostenfrei: Volltext: https://doi.org/10.1186/s40478-024-01759-2
 DOI: https://doi.org/10.1186/s40478-024-01759-2
Datenträger:Online-Ressource
Sprache:eng
Sach-SW:Artificial intelligence
 Copy number profiling
 Cryptocurrency miner
 Digital pathology
 Dimension reduction
 Edge computing
 Epigenetics
 gpGPU
 Intraoperative
 Methylation
 Methylation sequencing
 Methylome
 Microarray
 Nanopore sequencing
 Oncology
 Same-day classification
 SoC
 Tumour
 UMAP
 Unsupervised machine learning
K10plus-PPN:1907190031
Verknüpfungen:→ Zeitschrift

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