國家衛生研究院 NHRI:Item 3990099045/14199
English  |  正體中文  |  简体中文  |  全文笔数/总笔数 : 12145/12927 (94%)
造访人次 : 909049      在线人数 : 923
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
搜寻范围 查询小技巧：
  • 您可在西文检索词汇前后加上"双引号"，以获取较精准的检索结果
  • 若欲以作者姓名搜寻，建议至进阶搜寻限定作者字段，可获得较完整数据
    •  进阶搜寻
    主页 ‧ 登入 ‧ 上传 ‧ 说明 ‧ 关于NHRI ‧ 管理 到手机版


    jsp.display-item.identifier=請使用永久網址來引用或連結此文件: http://ir.nhri.org.tw/handle/3990099045/14199


    题名: A machine learning classifier for predicting stable MCI patients using gene biomarkers
    作者: Lin, RH;Wang, CC;Tung, CW
    贡献者: Institute of Biotechnology and Pharmaceutical Research
    摘要: Alzheimer's disease (AD) is a neurodegenerative disorder with an insidious onset and irreversible condition. Patients with mild cognitive impairment (MCI) are at high risk of converting to AD. Early diagnosis of unstable MCI patients is therefore vital for slowing the progression to AD. However, current diagnostic methods are either highly invasive or expensive, preventing their wide applications. Developing low-invasive and cost-efficient screening methods is desirable as the first-tier approach for identifying unstable MCI patients or excluding stable MCI patients. This study developed feature selection and machine learning algorithms to identify blood-sample gene biomarkers for predicting stable MCI patients. Two datasets obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were utilized to conclude 29 genes biomarkers (31 probes) for predicting stable MCI patients. A random forest-based classifier performed well with area under the receiver operating characteristic curve (AUC) values of 0.841 and 0.775 for cross-validation and test datasets, respectively. For patients with a prediction score greater than 0.9, an excellent concordance of 97% was obtained, showing the usefulness of the proposed method for identifying stable MCI patients. In the context of precision medicine, the proposed prediction model is expected to be useful for identifying stable MCI patients and providing medical doctors and patients with new first-tier diagnosis options.
    日期: 2022-04-15
    關聯: International Journal of Environmental Research and Public Health. 2022 Apr 15;19(8):Article number 4839.
    Link to: http://dx.doi.org/10.3390/ijerph19084839
    Cited Times(WOS): https://www.webofscience.com/wos/woscc/full-record/WOS:000785210500001
    Cited Times(Scopus): https://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85128177916
    显示于类别:[童俊維] 期刊論文

    文件中的档案:

    档案 描述 大小格式浏览次数
    ISI000785210500001.pdf996KbAdobe PDF181检视/开启


    在NHRI中所有的数据项都受到原著作权保护.

    TAIR相关文章

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - 回馈