More for less: predicting and maximizing genomic variant discovery via Bayesian nonparametrics

More for less: predicting and maximizing genomic variant discovery via Bayesian nonparametrics

<jats:title>Summary</jats:title> <jats:p>While the cost of sequencing genomes has decreased dramatically in recent years, this expense often remains nontrivial. Under a fixed budget, scientists face a natural trade-off between quantity and quality: spending resource...

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Bibliographic Details
Main Authors: Masoero, Lorenzo, Camerlenghi, Federico, Favaro, Stefano, Broderick, Tamara
Other Authors: Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Format: Article
Language:English
Published: Oxford University Press (OUP) 2022
Online Access:https://hdl.handle.net/1721.1/142893

Internet

https://hdl.handle.net/1721.1/142893

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