Next-generation phenotyping: introducing phecodeX for enhanced discovery research in medical phenomics.
Journal
Bioinformatics (Oxford, England)
ISSN: 1367-4811
Titre abrégé: Bioinformatics
Pays: England
ID NLM: 9808944
Informations de publication
Date de publication:
01 11 2023
01 11 2023
Historique:
received:
09
06
2023
revised:
13
09
2023
medline:
8
11
2023
pubmed:
6
11
2023
entrez:
6
11
2023
Statut:
ppublish
Résumé
Phecodes are widely used and easily adapted phenotypes based on International Classification of Diseases codes. The current version of phecodes (v1.2) was designed primarily to study common/complex diseases diagnosed in adults; however, there are numerous limitations in the codes and their structure. Here, we present phecodeX, an expanded version of phecodes with a revised structure and 1,761 new codes. PhecodeX adds granularity to phenotypes in key disease domains that are under-represented in the current phecode structure-including infectious disease, pregnancy, congenital anomalies, and neonatology-and is a more robust representation of the medical phenome for global use in discovery research. phecodeX is available at https://github.com/PheWAS/phecodeX.
Identifiants
pubmed: 37930895
pii: 7335839
doi: 10.1093/bioinformatics/btad655
pmc: PMC10627409
pii:
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : NHGRI NIH HHS
ID : R01 HG012657
Pays : United States
Organisme : NLM NIH HHS
ID : R01 LM010685
Pays : United States
Organisme : NICHD NIH HHS
ID : K12 HD043483
Pays : United States
Informations de copyright
© The Author(s) 2023. Published by Oxford University Press.
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