人工神经网络鉴定的用于预测GBM患者免疫治疗反应和生存获益的20基因panel
包含20个基因的panel，用于预测GBM患者免疫治疗反应和生存获益。
基本信息
资源简介
人工神经网络鉴定的用于预测GBM患者免疫治疗反应和生存获益的20基因panel
【数据集背景】
本数据集源站标题为《Artificial neural network identified a 20-gene panel with promising value in predicting immunotherapy response and survival benefits after anti-PD1/PD-L1 treatment in GBM patients》。项目资助方：The Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences (grant number: 2016-I2M-2-001)。源站首次发布：2021-02-25。
【数据内容】
源站原始描述：Question: Which biomarker can effectively predict the immunotherapy response and prognostic benefits of GBM patients, and how can potential responders to immune checkpoint inhibitors (ICIs) be discerned? Findings: In this retrospective, multiple-cohort study, we used two groups of ICI-treated GBM patients with large differences in survival benefits to define nonresponders and responders. Then, an artificial neural network was developed and showed excellent performance in predicting the immunothe…
【数据结构与技术规格】
数据集 DOI：10.6084/m9.figshare.13497855.v2。
【主题与分类】
源站学科分类：Bioinformatics and computational biology not elsewhere classified、Molecular targets、Central nervous system。主题标签：Artificial neural network、Immunotherapy biomarker、Immune Checkpoint Inhibitor、GBM patients tumors、Bioinformatics、Molecular Targets、Central Nervous System。
【适用方向】
组学数据适用于生物标志物发现与分子分型相关建模研究。
【获取与许可】
源站页面：https://figshare.com/articles/dataset/Artificial_neural_network_identified_a_20-gene_panel_with_promising_value_in_predicting_immunotherapy_response_and_survival_benefits_after_anti-PD1_PD-L1_treatment_in_GBM_patients/13497855
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Wang, Zihao (2020). Artificial neural network identified a 20-gene panel with promising value in predicting immunotherapy response and survival benefits after anti-PD1/PD-L1 treatment in GBM patients. figshare. Dataset
下载信息
注册下载
需要注册 Kaggle 账号并登录后下载，适合需要跟踪下载记录和使用 API 的用户。
暂未开放公开下载
无需注册即可直接获取公开样本或文档，适合快速预览和评估数据集质量。
免登录有偿下载
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：Artificial neural network identified a 20-gene panel with promising value in predicting immunotherapy response and survival benefits after anti-PD1/PD-L1 treatment in GBM patients
- DOI：10.6084/m9.figshare.13497855.v2
- 发布日期：2021-02-25
- 源站页面：https://figshare.com/articles/dataset/Artificial_neural_network_identified_a_20-gene_panel_with_promising_value_in_predicting_immunotherapy_response_and_survival_benefits_after_anti-PD1_PD-L1_treatment_in_GBM_patients/13497855
按页面 Download 按钮获取。
数据加载示例（图像类）
from PIL import Image
import glob, os
files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
+ glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
+ glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)
# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path) # 要求 子目录=类别
目录组织与标注格式以源站说明和下载后实际文件为准。
数据说明
Question: Which biomarker can effectively predict the immunotherapy response and prognostic benefits of GBM patients, and how can potential responders to immune checkpoint inhibitors (ICIs) be discerned?
Findings: In this retrospective, multiple-cohort study, we used two groups of ICI-treated GBM patients with large differences in survival benefits to define nonresponders and responders. Then, an artificial neural network was developed and showed excellent performance in predicting the immunotherapy response and prognostic benefits of GBM patients.
Meaning: The 20-gene panel could guide oncologists to accurately select potential responders for the preferential use of ICIs in GBM.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.6084/m9.figshare.13497855.v2（source: https://figshare.com/articles/dataset/Artificial_neural_network_identified_a_20-gene_panel_with_promising_value_in_predicting_immunotherapy_response_and_survival_benefits_after_anti-PD1_PD-L1_treatment_in_GBM_patients/13497855）。
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。
⚡️ 需要数据支持或标注服务？立即联系我们获取专业方案。
