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...

Πλήρης περιγραφή

Αποθηκεύτηκε σε:
Λεπτομέρειες βιβλιογραφικής εγγραφής
Τόπος έκδοσης:Bioinformatics
Κύριοι συγγραφείς: Zakeri, Mohsen, Srivastava, Avi, Almodaresi, Fatemeh, Patro, Rob
Μορφή: Artigo
Γλώσσα:Inglês
Έκδοση: Oxford University Press 2017
Θέματα:
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
Διαθέσιμο 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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