Discussion | The penalized generalized linear models are generally very good, and provide the added advantage of easy interpretation and relatively low model complexity; as noted in the previous paragraph, a softer regularization might be beneficial in the future. |
Results | As a linear transformation, the standardization does not affect linear models , though the additional preprocessing truncation to 60 has an appropriate impact on outliers. |
Supervised learning: Classification | To assess how much this discrimination depends on the classification approach utilized rather than the underlying information content in the data, we employed three different representative classification techniques: penalized logistic regression (a regularized generalized linear model based on Lasso), regularized random forest (a tree-based model), and support vector machine (a kernel-based model). |
Supervised learning: Regression | As With classification, the linear model dominates, and all methods perform similarly well With any of the input feature sets. |
Supervised learning: Regression | Once again the linear model dominates the nonlinear models, particularly for ADCC. |
Definition of kinetic signatures | The linear model is parameterised by the expression at time 0 (p 1) and the change in expression (p 2) from which the rate of increase or decrease can be calculated. |
Definition of kinetic signatures | The inference of model parameters from CAGE data for the early peak and linear models using nested sampling and the 11 based likelihood is illustrated in Fig 1C. |
Definition of kinetic signatures | CAGE clusters are assigned to one of the exponential kinetic signatures if log Z for that signature is greater than 10 times log Z for the linear model and log Z minus its standard deviation (sd) is greater than log Z plus the estimated sd for any other eXponential signature (nested sampling computes log Z for parameters mapped to O..1 and we used the resulting log Z for the unit cube for model comparison). |
Results | CAGE clusters were assigned to one of the exponential kinetic signatures or to the linear model according to the value of log Z. |
Results | An example of fitting early peak and linear models to an EGR1 time course is presented in Fig 1C. |