A Machine Learning Approach to Parkinson's Disease Blood Transcriptomics.
Parkinson’s disease
blood transcriptomics
feature selection
inflammation
machine learning
mitochondrial dysfunction
oxidative stress
xgboost
Journal
Genes
ISSN: 2073-4425
Titre abrégé: Genes (Basel)
Pays: Switzerland
ID NLM: 101551097
Informations de publication
Date de publication:
21 04 2022
21 04 2022
Historique:
received:
12
03
2022
revised:
16
04
2022
accepted:
18
04
2022
entrez:
28
5
2022
pubmed:
29
5
2022
medline:
1
6
2022
Statut:
epublish
Résumé
The increased incidence and the significant health burden associated with Parkinson's disease (PD) have stimulated substantial research efforts towards the identification of effective treatments and diagnostic procedures. Despite technological advancements, a cure is still not available and PD is often diagnosed a long time after onset when irreversible damage has already occurred. Blood transcriptomics represents a potentially disruptive technology for the early diagnosis of PD. We used transcriptome data from the PPMI study, a large cohort study with early PD subjects and age matched controls (HC), to perform the classification of PD vs. HC in around 550 samples. Using a nested feature selection procedure based on Random Forests and XGBoost we reached an AUC of 72% and found 493 candidate genes. We further discussed the importance of the selected genes through a functional analysis based on GOs and KEGG pathways.
Identifiants
pubmed: 35627112
pii: genes13050727
doi: 10.3390/genes13050727
pmc: PMC9141063
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
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