[转录学和"维度的诅咒":蒙特卡洛模拟的ML模型作为一种工具,用于分析多维数据在任务的搜索标记生物过程]
G J Osmak1,2,3, M V Pisklova1,2
1Chazov National Medical Research Center for Cardiology, Ministry of Health of the Russian Federation, Moscow, 121552 Russia.
Molekuliarnaia biologiia
|June 21, 2025
概括
本研究介绍了一种结合蒙特卡洛方法和机器学习的新算法,以减少高通量转录组数据中的维度. 它识别和权衡与疾病相关的重要基因,帮助研究和分析.
科学领域:
- 基因组学和生物信息学
- 计算生物学 计算生物学
相关概念视频
01:23Mechanistic Models: Compartment Models in Individual and Population Analysis
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
01:29Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...