相关实验视频
Updated: May 23, 2025
05:37
An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
2.0K
考克斯-赛奇:通过可解释图形的神经网络来增强考克斯的比例危险模型,用于癌症预后
Ruijun Mao1, Li Wan1, Minghao Zhou1
1College of Artificial Intelligence, Taiyuan University of Technology, 79 Yingze West Avenue, Wanbailin District, Taiyuan, Shanxi Province 030024, China.
Briefings in bioinformatics
|March 11, 2025
概括
我们开发了Cox-Sage,这是一种用于癌症预后的可解释深度学习模型. 它确定了影响患者生存风险的关键基因,改进了现有的生物标志物发现方法.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 深度学习 (DL) 模型为使用高通量测序数据的预后生物标志物发现提供了强大的工具.
- 然而,在癌症预后中,DL提取特征的解释性仍然是一个重大挑战.
- 现有的DL方法在预测模块设计中往往缺乏透明度.
研究的目的:
- 提出一个可解释的癌症预后模型,Cox-Sage,解决当前DL方法的局限性.
- 开发一种用于构建患者相似度图的方法,并嵌入基因表达数据以进行增强的预后建模.
- 引入用于量化基因重要性和识别预后生物标志物的新型指标.
主要方法:
- 从异质的临床数据构建一个患者相似度图.
- 将蛋白质编码基因表达数据作为特征嵌入到图节点中.
- 使用多层图形卷积来建模比例危险,并确保参数可解释性.
- 开发基因重要性指标:平均危险比率和平均危险比率的相反值.
主要成果:
- 与最先进的方法相比,Cox-Sage在七个TCGA数据集中表现出卓越的预后性能.
- 该模型成功地确定了LIHC (肝肝细胞癌) 数据集中的预后生物标志物.
- 提出的指标有效地区分了基因与高或低的癌症预后风险相关的基因,基于表达水平.
结论:
- 考克斯-赛奇为癌症预后和生物标志物发现提供了一个可解释的框架.
- 该模型解释参数含义的能力提高了信任和临床适用性.
- 这种方法通过将基于图形的DL与可解释的预后建模相结合,推动了该领域的发展.
相关概念视频
01:21Cancer Survival Analysis
318
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...
318
01:20Comparing the Survival Analysis of Two or More Groups
115
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
115
01:19The Mantel-Cox Log-Rank Test
271
The Mantel-Cox log-rank test is a widely used statistical method for comparing the survival distributions of two groups. It tests whether a statistically significant difference exists in survival times between the groups without assuming a specific distribution for the survival data, making it a non-parametric test. This flexibility makes the log-rank test particularly valuable in medical research and other fields where the timing of an event, such as death or disease recurrence, is of...
271
01:24Kaplan-Meier Approach
74
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
74
01:15Assumptions of Survival Analysis
83
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
83
01:18Introduction To Survival Analysis
153
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
The primary goal of survival analysis is to estimate survival time—the time...
153