Carrier probabilities were calculated in simple pedigrees for scenarios where the marker status of tumours is unknown, or is known in one or more members of a family.
However, the computation of this parameter takes much experimental work and cannot be calculated in a simple way.
At each learning step, several hidden nodes are randomly generated and among them the hidden node leading to the largest residual error decreasing will be added to the existing network and the output weight of the network will be calculated in a same simple way as in the original I-ELM.
Using such fingerprints, the similarity between two molecules can be calculated in a straightforward manner with simple similarity metrics such as Tanimoto [1], Dice [2], and so on.
It should be noticed that the Lyapunov function is not required to be calculated in this scheme; it is really simple and feasible in practical applications.
When a distribution is censored, as in Figures 7 to 11, then although a simple mean can be calculated in the traditional way, it makes more sense to consider the mean of the true or latent, underlying, distribution which had generated the censored distribution that was actually found.
Furthermore, even they could be calculated in quantity, it seems that the calculative way could not be that simple.
Pay can be calculated in many ways.
Hazard ratio (HR) and 95% confidence interval (CI) for infected and not infected were calculated in a simple Cox regression model with all patients and model excluding patients without infection before ECMO.
Error variance affected by Harvesters was evaluated in the following formula: Similarly, the following formula was used for error variance affected by ELISA operators: Error variance εijk that included sources of Harvest and ELISA was calculated in a simple one-way ANOVA model adjusted by specimen.
