These methods may generate sequences that may not be necessarily feasible [12].
In fact, integration of these two methods may generate some distinct benefits.
The comparison between the MAFU and traditional method shows that both methods may generate some ranking of project alternative based on the average utility value (i.e., the middle bar of the box plot), but the traditional method generates an overlapping confidence interval and min max ranges among the project alternatives, which complicates the decision marking and alternative selection process.
Although several studies demonstrated the presence of moderate to poor correlations between microarray and proteomic analyses [13], [14], [15], [16], implementing both methods may generate complementary and more informative data that cannot be obtained by either method alone.
Although it is difficult to judge which system offers more biologically relevant results, great caution should be taken when interpreting the interaction data as both two-hybrid screenings as well as biochemical methods may generate false positive and false negative interactions.
Each run of optimization methods may generate different results, depending on the initial settings.
Structure-solution methods may generate erroneous atoms that do not make chemical sense, which was observed in all cases.
Similarly, when the low plateau is missing, iterative methods may generate negative estimates for A and C.
Besides, PWM-based methods may generate too many false positive predictions when they are executed on a genome-wide scale [ 7, 8].
However, these methods may generate inconsistent results, due to the progression of various uncontrolled weight-bearing patterns, and result in generating unexpected and unsuitable bone-characteristic changes induced by primary OA.
