模型的非分歧性与稳定性分析
基于癌症干细胞智能体模型与强化学习自适应放疗的数据集，用于模拟肿瘤进展和治疗策略测试。
基本信息
资源简介
模型的非分歧性与稳定性分析
【数据集背景】
本数据集源站标题为《Non-divergence and stability analysis of model.》。源站首次发布：2026-02-05。
【数据内容】
源站原始描述：Cancer stem cells (CSCs) represent a rare but critical subpopulation within tumors, driving recurrence, resistance to therapy, and aggressive growth. To better understand CSC behavior in solid tumors, we developed a biologically constrained agent-based model (ABM) that simulates tumor progression initiated from a single CSC. The model incorporates essential microenvironmental factors—including oxygen diffusion, spatial limitations, stochastic migration, and cell cycle dynamics—allowing for high-…
【数据结构与技术规格】
文件数 1 个，合计 5.63 KB。Table 5.xls：application/vnd.ms-excel，5.63 KB。数据集 DOI：10.1371/journal.pone.0340426.t005。
【主题与分类】
源站学科分类：Medicine、Biological Sciences not elsewhere classified、Information Systems not elsewhere classified、Mathematical Sciences not elsewhere classified、Cancer。主题标签：intra tumoral heterogeneity、including oxygen diffusion、biologically constrained agent、time csc localization、scalable simulation environment、adaptive treatment strategies、fully optimize therapy、biologically grounded abm、single csc、resolution simulation。
【适用方向】
源站学科分类标注为 Medicine、Biological Sciences not elsewhere classified、Information Systems not elsewhere classified、Mathematical Sciences not elsewhere classified、Cancer。
【获取与许可】
源站页面：https://plos.figshare.com/articles/dataset/_p_Non-divergence_and_stability_analysis_of_model_p_/31269860
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Lagzian, Mina; Razavi, S. Ehsan; Moghaddam, Reyhane Kardehi (2026). Non-divergence and stability analysis of model.. 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：<p>Non-divergence and stability analysis of model.</p>
- DOI：10.1371/journal.pone.0340426.t005
- 发布日期：2026-02-05
- 文件数：1 个
- 文件总体积：5.63 KB
- 源站页面：https://plos.figshare.com/articles/dataset/_p_Non-divergence_and_stability_analysis_of_model_p_/31269860
命令行下载
curl -L -o "Table 5.xls" "https://ndownloader.figshare.com/files/61670465"
文件清单
Table 5.xls（5.63 KB）
文件总体积 5.63 KB（源站 API 实测）。
数据说明
Cancer stem cells (CSCs) represent a rare but critical subpopulation within tumors, driving recurrence, resistance to therapy, and aggressive growth. To better understand CSC behavior in solid tumors, we developed a biologically constrained agent-based model (ABM) that simulates tumor progression initiated from a single CSC. The model incorporates essential microenvironmental factors—including oxygen diffusion, spatial limitations, stochastic migration, and cell cycle dynamics—allowing for high-resolution simulation of tumor development and intra tumoral heterogeneity. While this work does not aim to fully optimize therapy for clinical application, it provides a flexible, scalable simulation environment where adaptive treatment strategies can be tested. To extend a biological model toward intelligent treatment, we integrated a reinforcement learning (Q-learning) component that adaptively adjusts radiation dosage based on real-time CSC localization and microenvironmental feedback. This component is currently presented as a proof-of-concept to demonstrate feasibility, and its optimization and convergence analysis will be explored in future studies. Our results suggest that reinforcement learning, when integrated with a biologically grounded ABM, can guide adaptive and more personalized radiotherapy strategies.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pone.0340426.t005（source: https://plos.figshare.com/articles/dataset/_p_Non-divergence_and_stability_analysis_of_model_p_/31269860）。
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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