利用人类表观基因组预测多因素疾病和症状的数据科学。

IF 3 4区 医学 Q2 GENETICS & HEREDITY
Epigenomics Pub Date : 2024-03-01 Epub Date: 2024-02-05 DOI:10.2217/epi-2023-0321
Shota Nishitani, Alicia K Smith, Akemi Tomoda, Takashi X Fujisawa
{"title":"利用人类表观基因组预测多因素疾病和症状的数据科学。","authors":"Shota Nishitani, Alicia K Smith, Akemi Tomoda, Takashi X Fujisawa","doi":"10.2217/epi-2023-0321","DOIUrl":null,"url":null,"abstract":"<p><p>Tweetable abstract This article reviews machine learning models that leverages epigenomic data for predicting multifactorial diseases and symptoms as well as how such models can be utilized to explore new research questions.</p>","PeriodicalId":11959,"journal":{"name":"Epigenomics","volume":" ","pages":"273-276"},"PeriodicalIF":3.0000,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Data science using the human epigenome for predicting multifactorial diseases and symptoms.\",\"authors\":\"Shota Nishitani, Alicia K Smith, Akemi Tomoda, Takashi X Fujisawa\",\"doi\":\"10.2217/epi-2023-0321\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Tweetable abstract This article reviews machine learning models that leverages epigenomic data for predicting multifactorial diseases and symptoms as well as how such models can be utilized to explore new research questions.</p>\",\"PeriodicalId\":11959,\"journal\":{\"name\":\"Epigenomics\",\"volume\":\" \",\"pages\":\"273-276\"},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2024-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Epigenomics\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.2217/epi-2023-0321\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/2/5 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q2\",\"JCRName\":\"GENETICS & HEREDITY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Epigenomics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.2217/epi-2023-0321","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/2/5 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"GENETICS & HEREDITY","Score":null,"Total":0}
引用次数: 0

摘要

Tweetable 摘要 本文回顾了利用表观基因组数据预测多因素疾病和症状的机器学习模型,以及如何利用这些模型探索新的研究问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Data science using the human epigenome for predicting multifactorial diseases and symptoms.

Tweetable abstract This article reviews machine learning models that leverages epigenomic data for predicting multifactorial diseases and symptoms as well as how such models can be utilized to explore new research questions.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Epigenomics
Epigenomics GENETICS & HEREDITY-
CiteScore
5.80
自引率
2.60%
发文量
95
审稿时长
>12 weeks
期刊介绍: Epigenomics provides the forum to address the rapidly progressing research developments in this ever-expanding field; to report on the major challenges ahead and critical advances that are propelling the science forward. The journal delivers this information in concise, at-a-glance article formats – invaluable to a time constrained community. Substantial developments in our current knowledge and understanding of genomics and epigenetics are constantly being made, yet this field is still in its infancy. Epigenomics provides a critical overview of the latest and most significant advances as they unfold and explores their potential application in the clinical setting.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信