一个集成的贝叶斯框架用于多omics预测和分类
Himel Mallick1,2, Anupreet Porwal3, Satabdi Saha4
1Division of Biostatistics, Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, 10065, New York, USA.
Statistics in medicine
|December 26, 2023
概括
这项研究引入了一种新的贝叶斯集合方法,用于整合多omics数据,通过量化不确定性来改善生物标志物发现和疾病预测. 该方法增强了对纵向和横截面数据的分析,以获得更好的临床见解.
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
- 系统生物学 系统生物学
背景情况:
- 多主题数据集成对于发现临床可行的生物标志物至关重要.
- 现有的方法用于从omics数据进行表型预测,往往缺乏整合纵向,多模式信息.
- 分析时间依赖的omics数据的分散框架是有限的.
研究的目的:
- 提出一种新的贝叶斯集合方法,用于整合来自多个纵向和横截面omics数据层的预测.
- 为了使从集成的奥米克数据中得出的预测中的不确定性量化和间隔估计.
- 为了解决现有的频率主义方法在处理复杂,时间依赖的多omics数据集方面的局限性.
主要方法:
- 开发了贝叶斯集体方法,以整合跨多种omics数据类型 (纵向和横截面) 的信息.
- 实施了一种允许在预测中量化不确定性的方法.
- 使用后部总结来对关键量进行间隔估计.
主要成果:
- 成功地将该方法应用于已发表的四个多omics数据集.
- 证明已知生物见解的回顾和发现新发现的发现.
- 在估计,预测和不确定性量化方面表现优于现有方法.
结论:
- 提出的贝叶斯集合方法有效地整合了多omics数据,以改善生物标志物发现和疾病预测.
- 该方法提供了可靠的不确定性量化,优于目前的方法.
- 开源软件可用,促进生物医学研究的更广泛应用.
相关概念视频
01:20Biostatistics: Overview
246
Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...
Discrete variables are...
246
01:03Prediction 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.
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.
2.3K
01:22Multi-input and Multi-variable systems
106
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
In the absence...
106
02:02Genomics
36.4K
Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
36.4K
01:06Model Approaches for Pharmacokinetic Data: Distributed Parameter Models
71
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
71
01:29Aggregates Classification
327
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
327