On statistical modeling of sequencing noise in high depth data to assess tumor evolution

article by Raúl Rabadán et al published July 2018 in Journal of Statistical Physics

On statistical modeling of sequencing noise in high depth data to assess tumor evolution is …
instance of (P31):
scholarly articleQ13442814

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P356DOI10.1007/S10955-017-1945-1
P932PMC publication ID6051708
P698PubMed publication ID30034030
P894zbMATH Open document ID1396.92039

P50authorRaúl RabadánQ7296181
Hossein KhiabanianQ55167918
Laura PasqualucciQ30303485
P2093author name stringGyan Bhanot
Nicholas Chiorazzi
Sonia Marsilio
P2860cites workMutational and structural analysis of diffuse large B-cell lymphoma using whole-genome sequencingQ37098851
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Tumor cells can follow distinct evolutionary paths to become resistant to epidermal growth factor receptor inhibitionQ36989342
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Rates and mechanisms of bacterial mutagenesis from maximum-depth sequencingQ37083023
P433issue1
P407language of work or nameEnglishQ1860
P921main subjectstatisticsQ12483
P6104maintained by WikiProjectWikiProject MathematicsQ8487137
P304page(s)143-155
P577publication date2018-07-01
P1433published inJournal of Statistical PhysicsQ1878468
P1476titleOn statistical modeling of sequencing noise in high depth data to assess tumor evolution
P478volume172