Analyses of the predictive value of p53 status for prediction of improvement in DFS from addition of docetaxel are shown in Figure 2a (p53 wild-type and mutant) and Figure 2b (p53 wild-type, missense and truncating mutations).
Again, this also allows us to make a prediction model for improvement with the predefined predictors.
We propose to use these mathematical predictions of improvements in survival for selections of protein stimulators/inhibitors to use with therapeutic intent.
While many pipelines now incorporate RNA-seq data in training GHMMs, there has been relatively little investigation into additionally combining RNA-seq data at the point of prediction, and room for improvement in this area motivates this study.
In moving towards improved agreement in OH*-profile predictions for all conditions, improvements in the kinetics mechanism were also realized at the two leaner equivalence ratios (ϕ = 1.0 and 0.5), both in terms of OH* profile shape and ignition delay times.
We can see that the use of TRF predictions indeed leaves space for improvement, with the best performing algorithm reducing the error rate by 37% compared to the baseline.
Comparison of our models with and without sickness absence diagnoses indicated that inclusion of diagnoses significantly improved the prediction of mortality (P for improvement in χ=0.03).
In the future, statistical and biological features of miRNA genes will be studied in detail to fully utilize these features for improvement of prediction algorithm.
Although our exposure models performed well, there is still room for improvement in prediction accuracy, especially for the EC, OC, and Si models, which had cross-validated R that could be improved upon.
As more accurate predictions become crucial for effective management and policy planning, new forecasting methods provide room for improvement.
While for droplet entrainment prediction, there is still large room for improvement.
