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...

Descripció completa

Guardat en:
Dades bibliogràfiques
Publicat a:Bioinformatics
Autors principals: Zakeri, Mohsen, Srivastava, Avi, Almodaresi, Fatemeh, Patro, Rob
Format: Artigo
Idioma:Inglês
Publicat: Oxford University Press 2017
Matèries:
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
Accés en línia: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
Etiquetes: Afegir etiqueta
Sense etiquetes, Sigues el primer a etiquetar aquest registre!

Ítems similars