In silico prediction of blood cholesterol levels from genotype data.
Journal
PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081
Informations de publication
Date de publication:
2020
2020
Historique:
received:
27
06
2019
accepted:
14
11
2019
entrez:
11
2
2020
pubmed:
11
2
2020
medline:
12
5
2020
Statut:
epublish
Résumé
In this work we present a framework for blood cholesterol levels prediction from genotype data. The predictor is based on an algorithm for cholesterol metabolism simulation available in literature, implemented and optimized by our group in the R language. The main weakness of the former simulation algorithm was the need of experimental data to simulate mutations in genes altering the cholesterol metabolism. This caveat strongly limited the application of the model in the clinical practice. In this work we present how this limitation could be bypassed thanks to an optimization of model parameters based on patient cholesterol levels retrieved from literature. Prediction performance has been assessed taking into consideration several scoring indices currently used for performance evaluation of machine learning methods. Our assessment shows how the optimization phase improved model performance, compared to the original version available in literature.
Identifiants
pubmed: 32040480
doi: 10.1371/journal.pone.0227191
pii: PONE-D-19-17026
pmc: PMC7010235
doi:
Substances chimiques
Cholesterol
97C5T2UQ7J
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e0227191Déclaration de conflit d'intérêts
The authors have declared that no competing interests exist.
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