特征消融研究
基于随机森林和PU学习的中性粒细胞分化调节因子识别特征消融数据集，包含基因表达与生理特征。
基本信息
资源简介
特征消融研究
【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 13.48 KB，主题涵盖 novel regulatory mechanism、mice induced neutropenia、hematopoietic stem cells、activated protein kinase、robust prediction model，可用于统计建模、特征工程与队列分析，CC BY 4.0 许可开放获取。
【数据集背景】
本数据集源站标题为《Feature ablation studies.》。源站首次发布：2025-03-17。
【数据内容】
源站原始描述：Neutrophils, an essential innate immune cell type with a short lifespan, rely on continuous replenishment from bone marrow (BM) precursors. Although it is established that neutrophils are derived from the granulocyte-macrophage progenitor (GMP), the molecular regulators involved in the differentiation process remain poorly understood. Here we developed a random forest-based machine-learning pipeline, NeuRGI (Neutrophil Regulatory Gene Identifier), which utilized Positive-Unlabeled Learning (PU-l…
【数据结构与技术规格】
文件数 1 个，合计 13.48 KB。pcbi.1012877.s005.xlsx：application/vnd.openxmlformats-officedocument.spreadsheetml.sheet，13.48 KB。数据集 DOI：10.1371/journal.pcbi.1012877.s005。
【主题与分类】
源站学科分类：Cell Biology、Genetics、Molecular Biology、Physiology、Environmental Sciences not elsewhere classified、Immunology。主题标签：novel regulatory mechanism、mice induced neutropenia、hematopoietic stem cells、activated protein kinase、robust prediction model、molecular regulators involved、identify neutrophil regulators、identified pipeline leads、proteins involved、model training。
【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://plos.figshare.com/articles/dataset/Feature_ablation_studies_/28610828
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Wang, Guihua; Zhang, Dan; He, Zhifeng; Mao, Bin; Hu, Xiao; Chen, Li; et al. (2025). Feature ablation studies.. PLOS Computational Biology. Dataset
【使用注意】
· 源站最后修改于 2025-03-31，引用时请注意版本时效。
下载信息
注册下载
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公开数据集受托下载与技术交付服务。
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：Feature ablation studies.
- DOI：10.1371/journal.pcbi.1012877.s005
- 发布日期：2025-03-17
- 文件数：1 个
- 文件总体积：13.48 KB
- 源站页面：https://plos.figshare.com/articles/dataset/Feature_ablation_studies_/28610828
命令行下载
curl -L -o "pcbi.1012877.s005.xlsx" "https://ndownloader.figshare.com/files/53051564"
文件清单
pcbi.1012877.s005.xlsx（13.48 KB）
文件总体积 13.48 KB（源站 API 实测）。
数据说明
Neutrophils, an essential innate immune cell type with a short lifespan, rely on continuous replenishment from bone marrow (BM) precursors. Although it is established that neutrophils are derived from the granulocyte-macrophage progenitor (GMP), the molecular regulators involved in the differentiation process remain poorly understood. Here we developed a random forest-based machine-learning pipeline, NeuRGI (Neutrophil Regulatory Gene Identifier), which utilized Positive-Unlabeled Learning (PU-learning) and neural network-based in silico gene knockout to identify neutrophil regulators. We interrogated features including gene expression dynamics, physiological characteristics, pathological relatedness, and gene conservation for the model training. Our identified pipeline leads to identifying Mitogen-Activated Protein Kinase-4 (MAP4K4) as a novel neutrophil differentiation regulator. The loss of MAP4K4 in hematopoietic stem cells and progenitors in mice induced neutropenia and impeded the differentiation of neutrophils in the bone marrow. By modulating the phosphorylation level of proteins involved in cell apoptosis, such as STAT5A, MAP4K4 delicately regulates cell apoptosis during the process of neutrophil differentiation. Our work presents a novel regulatory mechanism in neutrophil differentiation and provides a robust prediction model that can be applied to other cellular differentiation processes.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pcbi.1012877.s005（source: https://plos.figshare.com/articles/dataset/Feature_ablation_studies_/28610828）。
数据缺失？
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