以预测为导向的预后生物标志物发现与生存机器学习方法
Sijie Yao1, Biwei Cao1, Tingyi Li1
1Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA.
NAR genomics and bioinformatics
|June 19, 2023
概括
这项研究比较了机器学习方法来选择预后生物标志物来预测癌症存活率. 基于提升的方法在个性化治疗策略的复杂场景中显示出更高的准确性和更好的性能.
科学领域:
- 生物医学信息学 生物医学信息学
- 计算生物学 计算生物学
- 机器学习在瘤学中的应用
背景情况:
- 准确的预后生物标志物对于个性化癌症治疗策略至关重要.
- 癌症研究中的高维数据需要对预测模型进行有效的特征选择.
- 在生存模型中特征选择方法的性能需要进一步研究.
研究的目的:
- 使用先进的机器学习算法构建和比较以预测为导向的生物标志物选择框架.
- 评估不同机器学习方法在生存模型中预后生物标志物识别的有效性.
- 将以预测为导向的标记物选择 (PROMISE) 方法作为生存分析 (PROMISE-Cox) 的基准.
主要方法:
- 杆式机器学习算法:随机生存森林,极端梯度增强,光梯度增强和基于深度学习的生存模型.
- 将PROMISE方法作为生存模型 (PROMISE-Cox) 的基准.
- 进行模拟研究以评估性能指标,如准确性,真正率和假正率.
主要成果:
- 基于增强的方法 (极端梯度增强,光梯度增强) 显示出优异的预测准确性.
- 这些方法在复杂的场景中也显示出更好的真正阳性和假阳性率.
- 该研究使用拟议的策略在头癌数据中成功识别了预后生物标志物.
结论:
- 基于增强的机器学习方法在生存分析中的预后生物标志物选择中非常有效.
- 这些先进的技术提高了预测准确度,并减轻了高维癌症数据的过度匹配.
- 这些发现支持使用这些方法来开发瘤学个性化治疗策略.
相关概念视频
01:21Cancer Survival Analysis
402
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...
402
01:24Kaplan-Meier Approach
195
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,...
195
01:20Comparing the Survival Analysis of Two or More Groups
228
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...
228
01:19Survival Tree
118
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
Building a Survival Tree
Constructing a...
Building a Survival Tree
Constructing a...
118
01:18Introduction To Survival Analysis
298
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...
298
01:20Actuarial Approach
101
The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
101