Quantitative Framework for Model Evaluation in Microbiology Research Using
Pseudomonas aeruginosa
cystic fibrosis
infection
model
transcriptomics
Journal
mBio
ISSN: 2150-7511
Titre abrégé: mBio
Pays: United States
ID NLM: 101519231
Informations de publication
Date de publication:
14 01 2020
14 01 2020
Historique:
entrez:
16
1
2020
pubmed:
16
1
2020
medline:
18
9
2020
Statut:
epublish
Résumé
Laboratory models are a cornerstone of modern microbiology, but the accuracy of these models has not been systematically evaluated. As a result, researchers often choose models based on intuition or incomplete data. We propose a general quantitative framework to assess model accuracy from RNA sequencing data and use this framework to evaluate models of
Identifiants
pubmed: 31937646
pii: mBio.03042-19
doi: 10.1128/mBio.03042-19
pmc: PMC6960289
pii:
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : NHLBI NIH HHS
ID : R56 HL142857
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL123771
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL142587
Pays : United States
Organisme : NIGMS NIH HHS
ID : R01 GM116547
Pays : United States
Organisme : NHLBI NIH HHS
ID : R33 HL137077
Pays : United States
Informations de copyright
Copyright © 2020 Cornforth et al.
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