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Updated: Aug 16, 2026

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography
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Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Published on: August 16, 2020

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综合机器学习生存框架开发了一个基于线粒体功能和细胞死亡模式的交叉定义的预后模型,用于较低级别质瘤的大型多中心队列

Hu Qin1, Aimitaji Abulaiti1, Aierpati Maimaiti1

  • 1Department of Neurosurgery, Neurosurgery Centre, The First Affiliated Hospital of Xinjiang Medical University, No. 137, South Liyushan Road, Xinshi District, Urumqi City, 830054, Xinjiang, China.

Journal of translational medicine
|September 2, 2023
PubMed
概括

一个新的线粒体相关编程细胞死亡指数 (mtPCDI) 准确地预测了低度质瘤 (LGG) 患者的预后. 该指数来自机器学习,提供个性化的风险评估,并指导代谢和免疫治疗干预措施.

关键词:
较低级别的质瘤机器学习是机器学习.线粒体功能 线粒体功能精确瘤学是一门精确的专业.编程细胞死亡 编程细胞死亡

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