Machine learning in rare disease.
Journal
Nature methods
ISSN: 1548-7105
Titre abrégé: Nat Methods
Pays: United States
ID NLM: 101215604
Informations de publication
Date de publication:
Jun 2023
Jun 2023
Historique:
received:
16
03
2021
accepted:
22
04
2023
medline:
12
6
2023
pubmed:
30
5
2023
entrez:
29
5
2023
Statut:
ppublish
Résumé
High-throughput profiling methods (such as genomics or imaging) have accelerated basic research and made deep molecular characterization of patient samples routine. These approaches provide a rich portrait of genes, molecular pathways and cell types involved in disease phenotypes. Machine learning (ML) can be a useful tool for extracting disease-relevant patterns from high-dimensional datasets. However, depending upon the complexity of the biological question, machine learning often requires many samples to identify recurrent and biologically meaningful patterns. Rare diseases are inherently limited in clinical cases, leading to few samples to study. In this Perspective, we outline the challenges and emerging solutions for using ML for small sample sets, specifically in rare diseases. Advances in ML methods for rare diseases are likely to be informative for applications beyond rare diseases for which few samples exist with high-dimensional data. We propose that the method community prioritize the development of ML techniques for rare disease research.
Identifiants
pubmed: 37248386
doi: 10.1038/s41592-023-01886-z
pii: 10.1038/s41592-023-01886-z
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
803-814Subventions
Organisme : NHGRI NIH HHS
ID : R01 HG010067
Pays : United States
Informations de copyright
© 2023. Springer Nature America, Inc.
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