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Complementary analyses were carried out using bivariate association models, according to the decision tree represented in Figure 2b, for checking possible pleiotropic SNP affecting the content of a particular FA in both tissues.
Moreover, a series of analyses was carried out using bivariate models fitting single or two QTLs in the same chromosome, according to the decision tree represented in Figure 2a, for checking possible pleiotropic single QTL affecting the content of a particular FA in both tissues and other alternative hypothesis.
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Moreover, each stage corresponds to a decision tree representing the knowledge elicited from the expert.
In fact, each node of a soft decision tree represents a fuzzy subset of contextual factor space; therefore, each context belongs to several nodes with a membership degree.
Fragments retained as nodes in the Decision Tree represent good hypothesis candidates and all 400 nodes were converted into the corresponding structural hypotheses.
The decision tree represents the root cause analysis diagram and as such is read from top to bottom, and not side to side, or bottom to top (Hausmann et al. 2008).
This decision tree represents the main structure of the ICF classification [ 2].
In terms of quality of life, the decision tree represents the data as collected in the PLATO study.
Based on these decisions, the patient is assigned to a terminal leaf of each decision tree, representing a vote for either 'relapse' or 'no relapse'.
Therefore, the regulatory decision tree represents the combinatorial logic by which the TFs regulate the expression of the genes in the module under different treatment conditions.
Each branch of the decision tree represents one of the possible sequences of chance and decision events that could occur, which is in turn associated with a specific consequence that is valued based on its desirability.
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