To confirm the significance of the covariates identified by the logistic regression, and to rank their importance, we used the Bootstrap Inclusion Fraction (BIF) criterion [ 27].
Because all items were included in the original questionnaires because of their previously demonstrated importance, we used a five-point response scale that allowed discrimination between the higher scores for importance in the generic questionnaire: 1 = 'Not important', 2 = 'A little important', 3='Important', 4 = 'Very important', and 5 = 'Of utmost importance' [ 21].
In our implementation of the decision tree algorithm and the SNP importance score, we used the Gini impurity score.
To assess the impact of tree depth on each RF importance measure, we used the yeast eQTL data and recomputed eQTL maps for all expression traits, varying the nodesize argument to 5, 15, 29, 57, and 114.
In order to test our RALS setup with an analyte of significant importance, we have used DPA.
In an attempt to better define genetic differences across the major M. bovis lineages circulating in GB that may give rise to phenotypic differences of practical importance, we have used a combination of genome sequencing, transcriptome analyses, and recombinant DNA technology.
When analyzing the importance of plausibility, we used low plausibility estimates for different mortality outcomes.
To evaluate the level of functional importance of genes, we used two different parameters i) the level of adaptive codon bias (CAI) and ii) the rate of sequence evolution.
To explore the importance of monocytes/macrophages, we used a macrophage-deficient model achieved by diphtheria toxin (DT) treatment of mice selectively expressing the diphtheria toxin receptor in CD11b + cells.
To evaluate the selective or functional importance of genes, we used a parameter that measures the level of adaptive codon bias in E. coli (i.e. codon adaptive index, or CAI), and also estimates of evolutionary rates (Ka) between pairs of orthologs either in free-living bacteria or in pairs of symbionts.
To evaluate the selective or functional importance of genes, we used a parameter that measures the level of adaptive codon bias in E. coli (i.e. codon adaptive index, or CAI), and also estimates of evolutionary rates (Ka) between either pairs of orthologs in free-living bacteria or in pairs of symbionts.
