'strong relevance' is correct and can be used in written English.
It is used to refer to the importance or connection between two things. For example, "The strong relevance between health and food choices is clear."
According to our previous considerations [ 12, 13], a variable is relevant when its posterior of strong relevance is greater than or equal to 0.5.
On the other hand, redundancy means that two SNPs are somewhat interchangeable in the hypotheses of the strongly relevant variable sets (i.e. the probability of the joint strong relevance of two SNPs is lower than it is expected).
The results of the analysis of ALL risk are presented in Additional file 7. The most relevant SNPs and genes (i.e. with high posteriors for strong relevance) according to the BN-BMLA are presented in Table 5.
In this context, interaction means that two SNPs tend to occur together more often in the hypotheses of the strongly relevant variable sets than it is expected (i.e. the joint strong relevance of the two SNPs is more probable than it can be approximated from their univariate relevance).
Abandon features with strong relevance.
Therefore, ex situ feeding assays are a useful tool to detect impacts with strong relevance to the ecosystem level.
Moreover, the root-bias presents a much stronger relevance in Menéame, a platform which displayed the comments linearly regardless of the reply relationship.
Premises of modus ponens can be joined by fusion (denoted by ), which is like the application operation in the strongest relevance logic.
Based on equations (1), (2), and (3), the first gene in a group of marker genes (the gene associated with g = 1) has the strongest relevance among the members of that group of marker genes.
For example, identification of additional endophenotypes or certain aspects of currently known endophenotypes that have greater specificity for schizophrenia will provide new targets to model in animals that have stronger relevance to schizophrenia.
According to Definition 11 of a seed feature set, the front features in the seed feature set are of stronger relevance, which implies that they are less likely to be removed when Corollary 1 is applied.
