机器学习模型中的生物标志物
包含帕金森病患者的神经影像和脑脊液生物标志物，用于机器学习模型检测帕金森病及轻度认知障碍。
基本信息
资源简介
机器学习模型中的生物标志物
【数据集背景】
本数据集源站标题为《Biomarkers included in machine learning models.》。配套论文《Using machine learning for detection of Parkinson’s disease and mild cognitive impairment》发表于 PLOS One（2025），DOI：10.1371/journal.pone.0335541。源站首次发布：2025-11-19。
【数据内容】
源站原始描述：Background Parkinson’s disease is a movement disorder featuring motor symptoms and cognitive decline, which can manifest as mild cognitive impairment. The incidence of mild cognitive impairment increases with disease progression, and Parkinson’s disease can cause significant disability, therefore, identification of Parkinson’s disease and mild cognitive impairment in Parkinson’s disease is imperative. Neuroimaging and biofluid biomarkers have been studied separately, however, research suggests t…
【数据结构与技术规格】
文件数 1 个，合计 5.63 KB。Table 2.xls：application/vnd.ms-excel，5.63 KB。数据集 DOI：10.1371/journal.pone.0335541.t002。
【主题与分类】
源站学科分类：Medicine、Cell Biology、Neuroscience、Biotechnology、Chemical Sciences not elsewhere classified、Biological Sciences not elsewhere classified。主题标签：using machine learning、support vector machine、parkinson &# 8217、machine learning techniques、cause significant disability、xlink "> utilizing、xlink "> overall、xlink "> classification、mild cognitive impairment、cerebrospinal fluid biomarkers。
【适用方向】
多模态数据适合跨模态对齐、融合建模与联合表征学习方向的研究。
【获取与许可】
源站页面：https://plos.figshare.com/articles/dataset/Biomarkers_included_in_machine_learning_models_/30658128
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Dennis, Anthaea-Grace Patricia; Martin, Sarah L.; Chen, Robert; Gerretsen, Philip; Strafella, Antonio P. (2025). Biomarkers included in machine learning models.. PLOS ONE. Dataset
下载信息
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使用方式
数据集获取（Figshare）
- 源站标题：Biomarkers included in machine learning models.
- DOI：10.1371/journal.pone.0335541.t002
- 发布日期：2025-11-19
- 文件数：1 个
- 文件总体积：5.63 KB
- 源站页面：https://plos.figshare.com/articles/dataset/Biomarkers_included_in_machine_learning_models_/30658128
命令行下载
curl -L -o "Table 2.xls" "https://ndownloader.figshare.com/files/59699772"
文件清单
Table 2.xls（5.63 KB）
文件总体积 5.63 KB（源站 API 实测）。
数据说明
Background
Parkinson’s disease is a movement disorder featuring motor symptoms and cognitive decline, which can manifest as mild cognitive impairment. The incidence of mild cognitive impairment increases with disease progression, and Parkinson’s disease can cause significant disability, therefore, identification of Parkinson’s disease and mild cognitive impairment in Parkinson’s disease is imperative. Neuroimaging and biofluid biomarkers have been studied separately, however, research suggests that combining biomarkers may improve detection.
Objectives
We aimed to investigate using machine learning whether a combination of neuroimaging and biofluid biomarkers would result in more effective identification of Parkinson’s disease and mild cognitive impairment.
Methods
Utilizing the Parkinson’s Progression Markers Initiative dataset, we applied two different machine learning approaches, support vector machine and random forest, to explore combinations of neuroimaging and cerebrospinal fluid biomarkers for detection.
Results
Overall, both machine learning techniques had an equivalent performance. In general, in those models for detecting Parkinson’s disease, DaT-SPECT performed better than biofluid biomarkers. In models for detecting Parkinson’s disease patients with mild cognitive impairment, combining DaT-SPECT with phosphorylated-tau-181 resulted in higher accuracy, outperforming DaT-SPECT alone.
Conclusions
Classification for Parkinson’s disease and mild cognitive impairment may be improved by combining neuroimaging with biofluid biomarkers through machine learning models.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pone.0335541.t002（source: https://plos.figshare.com/articles/dataset/Biomarkers_included_in_machine_learning_models_/30658128）。
数据缺失？
依托陕西、四川两大基地，我们与超过十家三甲医院建立直接合作关系，覆盖合规授权、采集治理、专业标注、数据交付的全流程，为AI医疗团队提供即拿即用的高质量临床数据。
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