Urinary Metabolic Distinction of Niemann-Pick Class 1 Disease through the Use of Subgroup Discovery.
NMR analysis
NMR-linked metabolomics
Niemann–Pick disease type C1 (NPC1) disease
chemical pathology
evolutionary fuzzy systems
imbalanced data
subgroup discovery
Journal
Metabolites
ISSN: 2218-1989
Titre abrégé: Metabolites
Pays: Switzerland
ID NLM: 101578790
Informations de publication
Date de publication:
13 Oct 2023
13 Oct 2023
Historique:
received:
05
08
2023
revised:
19
09
2023
accepted:
03
10
2023
medline:
27
10
2023
pubmed:
27
10
2023
entrez:
27
10
2023
Statut:
epublish
Résumé
In this investigation, we outline the applications of a data mining technique known as Subgroup Discovery (SD) to the analysis of a sample size-limited metabolomics-based dataset. The SD technique utilized a supervised learning strategy, which lies midway between classificational and descriptive criteria, in which given the descriptive property of a dataset (i.e., the response target variable of interest), the primary objective was to discover subgroups with behaviours that are distinguishable from those of the complete set (albeit with a differential statistical distribution). These approaches have, for the first time, been successfully employed for the analysis of aromatic metabolite patterns within an NMR-based urinary dataset collected from a small cohort of patients with the lysosomal storage disorder Niemann-Pick class 1 (NPC1) disease (
Identifiants
pubmed: 37887404
pii: metabo13101079
doi: 10.3390/metabo13101079
pmc: PMC10608721
pii:
doi:
Types de publication
Journal Article
Langues
eng
Subventions
Organisme : Ministry of Science, Innovation and Universities, Spain
ID : PID2019-107793GB-I00/AEI/10.13039/501100011033
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