Models for prediction of single base primer extension efficiency from position and type of single mismatch in primer-template duplex.
Artificial neural network models
Multiple linear regression models
Single base extension
Journal
Heliyon
ISSN: 2405-8440
Titre abrégé: Heliyon
Pays: England
ID NLM: 101672560
Informations de publication
Date de publication:
Aug 2023
Aug 2023
Historique:
received:
12
01
2023
revised:
12
07
2023
accepted:
31
07
2023
medline:
14
8
2023
pubmed:
14
8
2023
entrez:
14
8
2023
Statut:
epublish
Résumé
Amplification and specificity of polymerase chain reaction (PCR) are affected by the position and type of primer-template mismatches (MMs) as well as various conditions of reaction. In this study, multiple linear regression (MLR) models and artificial neural network (ANN) models were developed for the prediction of the effects of primer-template mismatches on the primer extension efficiency in primer-template duplex. In MLR models, the independent variable
Identifiants
pubmed: 37576225
doi: 10.1016/j.heliyon.2023.e18829
pii: S2405-8440(23)06037-1
pmc: PMC10412835
doi:
Types de publication
Journal Article
Langues
eng
Pagination
e18829Informations de copyright
© 2023 The Authors. Published by Elsevier Ltd.
Déclaration de conflit d'intérêts
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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