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ID 117583
Title Alternative
Machine learning and GWAS for peripheral neuropathy
Author
Yamada, Hiroki Tokushima University
Ohmori, Rio Tokushima University
Keywords
genome-wide association study
peripheral neuropathy
vincristine
hematopoietic tumor
machine learning
Content Type
Journal Article
Description
Vincristine treatment may cause peripheral neuropathy. In this study, we identified the genes associated with the development of peripheral neuropathy due to vincristine therapy using a genome-wide association study (GWAS) and constructed a predictive model for the development of peripheral neuropathy using genetic information-based machine learning. The study included 72 patients admitted to the Department of Hematology, Tokushima University Hospital, who received vincristine. Of these, 56 were genotyped using the Illumina Asian Screening Array-24 Kit, and a GWAS for the onset of peripheral neuropathy caused by vincristine was conducted. Using Sanger sequencing for 16 validation samples, the top three single nucleotide polymorphisms (SNPs) associated with the onset of peripheral neuropathy were determined. Machine learning was performed using the statistical software R package “caret.” The 56 GWAS and 16 validation samples were used as the training and test sets, respectively. Predictive models were constructed using random forest, support vector machine, naive Bayes, and neural network algorithms. According to the GWAS, rs2110179, rs7126100, and rs2076549 were associated with the development of peripheral neuropathy on vincristine administration. Machine learning was performed using these three SNPs to construct a prediction model. A high accuracy of 93.8% was obtained with the support vector machine and neural network using rs2110179 and rs2076549. Thus, peripheral neuropathy development due to vincristine therapy can be effectively predicted by a machine learning prediction model using SNPs associated with it.
Journal Title
The Pharmacogenomics Journal
ISSN
14731150
1470269X
NCID
AA11703518
Publisher
Springer Nature
Volume
22
Issue
4
Start Page
241
End Page
246
Published Date
2022-06-25
EDB ID
DOI (Published Version)
URL ( Publisher's Version )
FullText File
language
eng
TextVersion
Author
departments
Pharmaceutical Sciences
Medical Sciences
University Hospital