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相关概念视频

Survival Tree01:19

Survival Tree

109
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...
109
Prediction Intervals01:03

Prediction Intervals

2.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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Introduction To Survival Analysis01:18

Introduction To Survival Analysis

276
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...
276
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

153
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.
153
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

377
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
377
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

233
Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
233

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相关实验视频

Updated: Jul 17, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
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Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

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用高维特征预测生存的REFINED-CNN框架.

Omid Bazgir1, James Lu1

  • 1Modeling & Simulation/Clinical Pharmacology, Genentech, 1 DNA Way, South San Francisco, CA 94080, USA.

iScience
|September 4, 2023
PubMed
概括

本研究引入了REFINED-CNN,用于在临床试验中使用基因组学数据进行准确的生存预测. 该模型增强了预测性能,并提供了对基因对患者结果的重要性的可解释的见解.

科学领域:

  • 基因组学就是基因组学.
  • 机器学习 机器学习
  • 药物基因组学 药物基因组学
  • 生物信息学是一种生物信息学.

背景情况:

  • 在使用高通量基因组学数据的临床试验中准确预测生存率至关重要,但具有挑战性.
  • 当前的机器学习模型往往缺乏预测性能和可解释性.

研究的目的:

  • 扩展REFINED-CNN模型使用RNA测序数据进行生存预测.
  • 改善临床试验生存分析中的预测性能和模型解释性.

主要方法:

  • 将高维RNA测序数据映射到精制图像中,用于卷积神经网络 (CNN) 建模.
  • 利用转移学习来调整REFINED-CNN生存模型以适应具有有限患者数据的新癌症类型.
  • 使用风险评分反向传播来量化本地和全球特征 (基因) 重要性.

主要成果:

  • 在REFINED-CNN的生存模型证明了强大的和准确的预测.
  • 该模型显示通过转移学习有效适应新任务和癌症类型.
  • 风险评分的反向传播为预测生存提供了对特征重要性的可解释的见解.

结论:

关键词:
癌症 癌症 癌症 癌症基因组学就是基因组学.机器学习是机器学习.

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  • 在药物基因组学中,REFINED-CNN为生存预测提供了一种强大而可解释的方法.
  • 该模型利用转移学习和提供特征重要性的能力提高了其临床实用性.
  • 这种方法推进了深度学习的应用,用于分析临床试验结果的基因组学数据.