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Percent phenotypic variance attributable to a locus was estimated by modeling the binary trait as continuous.
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The molecular geometry of the Top1 non-covalent binary complex was obtained by modelling the 1A36 PDB entry; after removing the DNA, the same structure was used to obtain a model of the Top1 enzyme in the absence of DNA.
This can be done by modeling it as repeated binary trait (each breeding season).
Because of the challenge posed by modeling an arbitrary number of different binary markers of cell cycle progression, we need to devise a new, general sampling model.
The first model is the binary reception model employed by the Unit Disk Graph (UDG) model.
First we utilize the logistic regression model to distinguish the most important symptoms of influenza infection by fitting a logistic regression model to the binary influenza infection outcome in the sample, using binary indicators of the influenza-like symptoms as predictors.
The bubble size distribution predicted by the binary model is represented well by the geometric and exponential distributions for a large number of bubbles and coalescence events.
Error-prone channel is considered by adopting the binary symmetric model is for performance comparison.
Firstly, seroprevalence by age group was determined by fitting the binary mixture model to 200 animals per age category.
Molecular structural attributes are modeled by binary variables and the process variables are continuous variables.
The effect of learning on the synaptic connections is modeled by binary random variables that take a value s i j = 1 if the putative synapse from neuron j to neuron i is in a potentiated state (is able to transmit signals), whereas s i j = 0 if it is inactive and cannot contribute to the postsynaptic depolarization.
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