| Item type |
文献 / Documents(1) |
| 公開日 |
2022-10-18 |
| アクセス権 |
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アクセス権 |
open access |
| 資源タイプ |
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資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
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資源タイプ |
journal article |
| 出版社版DOI |
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|
関連識別子 |
https://doi.org/10.1038/s41397-022-00282-8 |
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関連名称 |
10.1038/s41397-022-00282-8 |
| 出版タイプ |
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|
出版タイプ |
AM |
|
出版タイプResource |
http://purl.org/coar/version/c_ab4af688f83e57aa |
| タイトル |
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|
タイトル |
A machine learning model using SNPs obtained from a genome-wide association study predicts the onset of vincristine-induced peripheral neuropathy |
| タイトル別表記 |
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その他のタイトル |
Machine learning and GWAS for peripheral neuropathy |
| 著者 |
ヤマダ, ヒロキ
オオモリ, リオ
岡田, 直人
中村, 信元
賀川, 久美子
藤井, 志朗
三木, 浩和
石澤, 啓介
安倍, 正博
佐藤, 陽一
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| 抄録 |
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内容記述 |
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. |
| キーワード |
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|
主題 |
genome-wide association study |
| キーワード |
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|
主題 |
peripheral neuropathy |
| キーワード |
|
|
主題 |
vincristine |
| キーワード |
|
|
主題 |
hematopoietic tumor |
| キーワード |
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|
主題 |
machine learning |
| 書誌情報 |
en : The Pharmacogenomics Journal
巻 22,
号 4,
p. 241-246,
発行日 2022-06-25
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| 収録物ID |
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収録物識別子タイプ |
ISSN |
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収録物識別子 |
14731150 |
| 収録物ID |
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|
収録物識別子タイプ |
ISSN |
|
収録物識別子 |
1470269X |
| 収録物ID |
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|
収録物識別子タイプ |
NCID |
|
収録物識別子 |
AA11703518 |
| 出版者 |
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|
出版者 |
Springer Nature |
| EID |
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|
識別子 |
386499 |
| 言語 |
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|
言語 |
eng |
pj_22_4_241.pdf (881 KB)
