A proof of concept of a machine learning algorithm to predict late-onset 21-hydroxylase deficiency in children with premature pubic hair - Sorbonne Université
Journal Articles (Data Paper) Journal of Steroid Biochemistry and Molecular Biology Year : 2022

A proof of concept of a machine learning algorithm to predict late-onset 21-hydroxylase deficiency in children with premature pubic hair

Héléna Agnani
  • Function : Author
Guillaume Bachelot
Thibaut Eguether
  • Function : Author
Sorbonne Université
Centre de Recherche Saint-Antoine
Bettina Ribault
  • Function : Author
Jean Fiet
  • Function : Author
Yves Le Bouc
  • Function : Author
Irène Netchine
  • Function : Author
Muriel Houang
  • Function : Author
Antonin Lamazière

Domains

Life Sciences [q-bio]
Fichier principal
Vignette du fichier
S096007602200036X.pdf (603.31 Ko) Télécharger le fichier
Origin Files produced by the author(s)

Accord Elsevier CCSD  : Connect in order to contact the contributor

https://hal.sorbonne-universite.fr/hal-03972777

Submitted on : Monday, July 22, 2024-11:23:09 AM

Last modification on : Monday, July 22, 2024-11:23:11 AM

Download

Dates and versions

hal-03972777 , version 1 (22-07-2024)

Licence

Attribution - NonCommercial

Identifiers

Elsevier-hosted Accepted Manuscript

Cite

Héléna Agnani, Guillaume Bachelot, Thibaut Eguether, Bettina Ribault, Jean Fiet, et al.. A proof of concept of a machine learning algorithm to predict late-onset 21-hydroxylase deficiency in children with premature pubic hair. Journal of Steroid Biochemistry and Molecular Biology, 2022, 220, pp.106085. ⟨10.1016/j.jsbmb.2022.106085⟩. ⟨hal-03972777⟩

Export

BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite

Collections

U938 SORBONNE-UNIVERSITE SU-MEDECINE
53 View
5 Download

Altmetric

Share

More