Improved data-driven likelihood factorizations for transcript abundance estimation

MOTIVATION: Many methods for transcript-level abundance estimation reduce the computational burden associated with the iterative algorithms they use by adopting an approximate factorization of the likelihood function they optimize. This leads to considerably faster convergence of the optimization pr...

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Opis bibliograficzny
Wydane w:Bioinformatics
Główni autorzy: Zakeri, Mohsen, Srivastava, Avi, Almodaresi, Fatemeh, Patro, Rob
Format: Artigo
Język:Inglês
Wydane: Oxford University Press 2017
Hasła przedmiotowe:
Ismb/Eccb 2017: The 25th Annual Conference Intelligent Systems for Molecular Biology Held Jointly with the 16th Annual European Conference on Computational Biology, Prague, Czech Republic, July 21–25, 2017
Dostęp online:https://ncbi.nlm.nih.gov/pmc/articles/PMC5870700/
https://ncbi.nlm.nih.gov/pubmed/28881996
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1093/bioinformatics/btx262
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