Nonetheless, further studies that examine within population diversity and recent evolutionary changes in selective constraints at these genes may address this question in distinguishing between competing selection models.
It is to be noted that the two competing selection models mentioned above both imply that the fast-acetylator phenotype began to suffer a selective disadvantage.
The balance between these competing selection pressures depends in part on their relative benefits and costs.
Moreover, populations may benefit from continued exposure to such selection in order to maintain overall adaptation under the influence of potential competing selection at other life stages.
Given the multiple competing selection pressures and the fact that bacteria phage encounter rates in nature are likely to be dramatically different than those observed in liquid microcosms, it is unclear exactly how important of an evolutionary force phages are in shaping natural microbial populations.
If two individuals do compete, selection pressure is strong for the black (if the die-roll is 1, the gray survives; if 2 6, the black survives).
Results also show that gene subsets generated by modified AHP lead to greater accuracy and stability compared to competing gene selection methods, i.e. information gain, symmetrical uncertainty, Bhattacharyya distance, and ReliefF.
Similarly, KNN and SVM give higher accuracy for all the competing feature selection methods except chisquare.
As shown in Table 2, the EFS-MI gives higher classification accuracy than all other competing feature selection methods with KNN and decision trees classifiers.
Similarly, on TUIDS dataset, classification accuracy of our method is higher than all the competing feature selection methods with decision trees, random forest and KNN classifiers.
