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

DOI10.1007/s10955-017-1945-1zbMath1396.92039OpenAlexW2780329783WikidataQ60146450 ScholiaQ60146450MaRDI QIDQ1668966

Hossein Khiabanian, Laura Pasqualucci, Nicholas Chiorazzi, Sonia Marsilio, Gyan Bhanot, Raúl Rabadán

Publication date: 29 August 2018

Published in: Journal of Statistical Physics (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc6051708

zbMATH Keywords

drug resistancestatistical modelingcancer mortalityhigh depth datasequencing noisetherapy-resistent diseasetumor evolution

Mathematics Subject Classification ID

Medical applications (general) (92C50)

Related Items (1)

A Bayesian nonparametric model for inferring subclonal populations from structured DNA sequencing data

Uses Software

• MERIT
• BWA
• PEAR

Cites Work

• Unnamed Item
• On the convolution of the negative binomial random variables
• Detecting mutations in mixed sample sequencing data using empirical Bayes
• On the Sums of Compound Negative Binomial and Gamma Random Variables
• Large population solution of the stochastic Luria–Delbrück evolution model

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