基于代谢组学和堆叠机器学习的人体深静脉血栓诊断新策略数据集
包含DVT患者血清双平台代谢组学数据，基于61种差异代谢物和SHAP特征工程构建堆叠机器学习诊断模型，用于快速准确诊断深静脉血栓。
基本信息
资源简介
基于代谢组学和堆叠机器学习的人体深静脉血栓诊断新策略数据集
【一句话概览】
本数据集是 figshare 公开的结构化表格数据集，收录 1 个文件、合计 16.00 MB，主题涵盖 various pathological conditions、shapley additive explanations、serious health issue、machine learning methods、presents considerable challenges，可用于统计建模、特征工程与队列分析，CC BY-NC 4.0 许可开放获取。
【数据集背景】
本数据集源站标题为《Novel Strategy
for Human Deep Vein Thrombosis Diagnosis
Based on Metabolomics and Stacking Machine Learning》。配套论文《Novel Strategy for Human Deep Vein Thrombosis Diagnosis Based on Metabolomics and Stacking Machine Learning》发表于 Analytical Chemistry（2024），DOI：10.1021/acs.analchem.4c02973。源站首次发布：2024-08-28。
【数据内容】
源站原始描述：Deep vein thrombosis (DVT) is a serious health issue that often leads to considerable morbidity and mortality. Diagnosis of DVT in a clinical setting, however, presents considerable challenges. The fusion of metabolomics techniques and machine learning methods has led to high diagnostic and prognostic accuracy for various pathological conditions. This study explored the synergistic potential of dual-platform metabolomics (specifically, gas chromatography–mass spectrometry (GC-MS) and liquid chro…
【数据结构与技术规格】
文件数 1 个，合计 16.00 MB。数据集 DOI：10.1021/acs.analchem.4c02973.s003。
【主题与分类】
源站学科分类：Biochemistry、Space Science、Medicine、Biotechnology、Chemical Sciences not elsewhere classified、Biological Sciences not elsewhere classified。主题标签：various pathological conditions、shapley additive explanations、serious health issue、machine learning methods、presents considerable challenges、one differential metabolites、five key metabolites、stacking diagnostic model、diagnostic model、considerable morbidity。
【适用方向】
结构化表格数据可直接用于统计建模、特征工程与队列分析。
【获取与许可】
源站页面：https://acs.figshare.com/articles/dataset/Novel_Strategy_for_Human_Deep_Vein_Thrombosis_Diagnosis_Based_on_Metabolomics_and_Stacking_Machine_Learning/26866656
使用许可：CC BY-NC 4.0（https://creativecommons.org/licenses/by-nc/4.0/）。
规范引用：Cao, Jie; An, Guo-shuai; Li, Rong-qi; Hou, Ze-jin; Li, Jian; Jin, Qian-qian; et al. (2024). Novel Strategy for Human Deep Vein Thrombosis Diagnosis Based on Metabolomics and Stacking Machine Learning. ACS Publications. Dataset
下载信息
注册下载
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提供高速下载与技术交付服务（收技术服务费，非数据销售）
暂未开放当前数据集主要来源为 Kaggle 公开托管，完整影像包建议通过原始链接或 Kaggle API 获取。
使用方式
数据集获取（Figshare）
- 源站标题：Novel Strategy
for Human Deep Vein Thrombosis Diagnosis
Based on Metabolomics and Stacking Machine Learning - DOI：10.1021/acs.analchem.4c02973.s003
- 发布日期：2024-08-28
- 文件数：1 个
- 文件总体积：16.00 MB
- 源站页面：https://acs.figshare.com/articles/dataset/Novel_Strategy_for_Human_Deep_Vein_Thrombosis_Diagnosis_Based_on_Metabolomics_and_Stacking_Machine_Learning/26866656
命令行下载
curl -L -o "ac4c02973_si_003.xlsx" "https://ndownloader.figshare.com/files/48866874"
文件清单
ac4c02973_si_003.xlsx（16.00 MB）
文件总体积 16.00 MB（源站 API 实测）。
数据说明
Deep vein thrombosis (DVT) is a serious health issue
that often
leads to considerable morbidity and mortality. Diagnosis of DVT in
a clinical setting, however, presents considerable challenges. The
fusion of metabolomics techniques and machine learning methods has
led to high diagnostic and prognostic accuracy for various pathological
conditions. This study explored the synergistic potential of dual-platform
metabolomics (specifically, gas chromatography–mass spectrometry
(GC-MS) and liquid chromatography–mass spectrometry (LC-MS))
to expand the detection of metabolites and improve the precision of
DVT diagnosis. Sixty-one differential metabolites were identified
in serum from DVT patients: 22 from GC-MS and 39 from LC-MS. Among
these, five key metabolites were highlighted by SHapley Additive exPlanations
(SHAP)-guided feature engineering and then used to develop a stacking
diagnostic model. Additionally, a user-friendly interface application
system was developed to streamline and automate the application of
the diagnostic model, enhancing its practicality and accessibility
for clinical use. This work showed that the integration of dual-platform
metabolomics with a stacking machine learning model enables faster
and more accurate diagnosis of DVT in clinical environments.
许可
CC BY-NC 4.0
引用
学术使用请引用 DOI 10.1021/acs.analchem.4c02973.s003（source: https://acs.figshare.com/articles/dataset/Novel_Strategy_for_Human_Deep_Vein_Thrombosis_Diagnosis_Based_on_Metabolomics_and_Stacking_Machine_Learning/26866656）。
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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