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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Detalles Bibliográficos
Autores principales: Zakeri, Mohsen, Srivastava, Avi, Almodaresi, Fatemeh, Patro, Rob
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2017
Materias:
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
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5870700/
https://www.ncbi.nlm.nih.gov/pubmed/28881996
http://dx.doi.org/10.1093/bioinformatics/btx262

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5870700/
https://www.ncbi.nlm.nih.gov/pubmed/28881996
http://dx.doi.org/10.1093/bioinformatics/btx262

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