相关实验视频

Updated: Jul 10, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.2K

一种以数据为中心的机器学习方法,使用低失衡的TCGA数据来改善结质瘤等级的预测

Raquel Sánchez-Marqués1,2, Vicente García3, J Salvador Sánchez4

  • 1Fundación Estatal, Salud, Infancia y Bienestar Social, 28029, Madrid, Spain.

Scientific reports
|July 26, 2024
PubMed
概括

这项研究的重点是通过提高数据质量而不是仅仅通过算法来改善质瘤等级预测. 数据的标准化和平衡改善了机器学习模型的性能,特别是在组合方法中.

关键词:
阶级不平衡造成的失衡临床因素 临床因素以数据为中心的机器学习.功能排名 功能排名质瘤等级的质瘤等级.分子生物标志物 分子生物标志物

更多相关视频

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.8K
Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography09:53

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

7.2K

相关概念视频

Cancer Survival Analysis01:21

Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...