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Mutual Hazard Networks: Markov chain models of cancer progression

URN to cite this document:
urn:nbn:de:bvb:355-epub-534170
DOI to cite this document:
10.5283/epub.53417
Schill, Rudolf
Date of publication of this fulltext: 14 Dec 2022 12:23


Abstract (English)

Cancer progresses by accumulating genomic events, such as mutations and copy number alterations, whose chronological order is key to understanding the disease but difficult to observe. Instead, cancer progression models use co-occurrence patterns in cross-sectional data to infer dependencies between events and thereby uncover their most likely order of occurrence. Here we introduce Mutual ...

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Translation of the abstract (German)

Krebs schreitet fort, indem er genomische Ereignisse wie Mutationen und Kopienzahländerungen anhäuft. Ihre chronologische Reihenfolge ist der Schlüssel zum Verständnis der Krankheit, aber schwer zu beobachten. Stattdessen verwenden Krebsprogressionsmodelle Muster von gemeinsam aufgetretenen Ereignissen in Querschnittsdaten, um auf Abhängigkeiten zwischen Ereignissen zu schließen und dadurch ihre ...

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