Contrary to their approach, we utilize more general set theory to define operations on our IntentScopes.
In the first approach we utilize a model-based, reduced, composite representation of the electric conductivity that is highly flexible, even for a moderate number of degrees of freedom.
A small minority of this group also includes species found in the Atlantic Ocean with greater geographic ranges, an artifact of the conservative linear approach we utilize (see methods).
To test how well the approximate Bayes approach emulated the fully Bayesian approach we utilize the Deviance Information Criterion (DIC) ([ 28]), which is an ad-hoc alternative to Bayes' factor that involves the likelihood and a penalty term.
In our approach, we utilize information on STRING database and expression data to construct a weighted network and to decipher the active networks related to drug resistance using A* search method.
In our approach, we utilize the data variability by exploiting mutual information values which are too weak in the whole data set to either pass a statistical test or which are not the maximum mutual information value for any gene.
