library(nlme) # Fit Gaussian linear and nonlinear mixed-effects models library(lme4) # Fit linear and generalized linear mixed-effects models library(epiR) # Analysis of epidemiological data library(epicalc) # Functions for epidemiological calculations library(lattice) # Data visualization system library...
Lee, S., & Xing, E. P. (2012). Leveraging input and output structures for joint mapping of epistatic and marginal eQTLs. Bioinformatics, 28, i137-i146. Lippert, C., et al. (2011). FaST linear mixed models for genome-wide association studies. Nat. Methods, 8, 833-835. Listgarten,...
Lee, S., & Xing, E. P. (2012). Leveraging input and output structures for joint mapping of epistatic and marginal eQTLs.Bioinformatics, 28, i137-i146. Lippert, C., et al. (2011). FaST linear mixed models for genome-wide association studies.Nat. Methods, 8, 833-835. Listgarten, J...
Linear mixed model fit by REML ['lmerMod'] Formula: CXCL1 ~ distance2 + (1 | patient) Data: df REML criterion at convergence: 456 Scaled residuals: Min 1Q Median 3Q Max -1.91585 -0.89480 -0.05695 0.74833 2.34680 Random effects: Groups Name Variance Std.Dev. patient (Intercept) 0.01888 ...
Python用Lasso改进线性混合模型Linear Mixed Model分析拟南芥和小鼠复杂性状遗传机制多标记表型预测可视化,引言人类、动植物中诸多数量性状虽具遗传性,但人们对其潜在遗传结构的全面认识仍不足。像全基因组关联研究和连锁图谱分析虽已揭示出部分控制性状变异的因果变体,
Python用Lasso改进线性混合模型Linear Mixed Model分析拟南芥和小鼠复杂性状遗传机制多标记表型预测可视化 全文链接:https://tecdat.cn/?p=38800 原文出处:拓端数据部落公众号 在生物医学领域,探究可遗传性状的遗传基础是关键挑战之一。对于受多基因位点多因素控制的性状,准确检测其关联存在诸多困难,且易受群体结构等混杂...
Lippert, C., et al. (2011). FaST linear mixed models for genome-wide association studies. _Nat. Methods_, 8, 833-835. Listgarten, J., et al. (2010). Correction for hidden confounders in the genetic analysis of gene expression. _Proc. Natl Acad. Sci. USA_, 107, 16465-16470. ...
Python用Lasso改进线性混合模型Linear Mixed Model分析拟南芥和小鼠复杂性状遗传机制多标记表型预测可视化 全文链接:https://tecdat.cn/?p=38800 *原文出处:拓端数据部落公众号* 在生物医学领域,探究可遗传性状的遗传基础是关键挑战之一。对于受多基因位点多因素控制的性状,准确检测其关联存在诸多困难,且易受群体结构等...
from interpret.ext.blackbox import MimicExplainer # you can use one of the following four interpretable models as a global surrogate to the black box model from interpret.ext.glassbox import LGBMExplainableModel from interpret.ext.glassbox import LinearExplainableModel from interpret.ext.glassbox i...
from sklearn import linear_model linear_model.LinearRegression() #调用线性回归模型 Matplotlib 它是Python强大的数据可视化工具、2D绘图库,可以轻松生成简单而强大的可视化图形,可以绘制散点图、折线图、饼状图等图形。但其库本身过于复杂,绘制的图需要大量的调整才能变精致。