Figure 4.

Amount of experimental noise affects estimates of ${〈R^2〉}_{LG}$ and model performance. Analysis of the effect of experimental noise in the response variables on the ${〈R^2〉}_{LG}$ upper bound estimates (black) and predictive performance of ML models (red) with the case of a large yeast multi-experiment transcriptomics dataset. ²³ The noise level was varied by adjusting the number of data replicates with random sampling (inset figure). Lines and shaded areas depict means and standard deviations of the 30 measurements per each n replicates, depicted as points. ${〈R^2〉}_{LG}$ is the expected upper bound for the coefficient of determination $R^2$ derived in this study (see section “Estimating the theoretical upper bound of regression model performance”), CV denotes cross validations.