Fast sequence comparisons, data mining, and genetic algorithms further promote these procedures.
Aggregation of comparisons data to rank experimental results and take decisions is being more and more practiced in diverse areas, spanning over a variety of disciplines including, e.g., quality function deployment in industrial engineering, scientometrics, and recovery rate testing of new medications.
For group comparisons, data were analyzed with the t-test for continuous data, and the Chi-square test for categorical data.
To allow the model to make appropriate comparisons, data from the remaining HTS assays was examined to ensure there was the expected normal distribution around zero % inhibition.
To gain a sufficient participant group size to make comparisons, data were collected from graduating seniors during the last fourteen days prior to the official graduation date of the spring 2006, summer 2006, fall 2006, and spring 2007 semesters.
Due to the wide range of livestock species owned by households, in order to make valid comparisons, data on the total number of livestock were computed to a standardised measure - tropical livestock units (TLUs).
Tests for the full-population dataset gave similar conclusions to the above comparisons (data not shown).
For 3-group comparisons, data were analyzed by ANOVA followed by Dunnett's test.
There were no significant correlations for any of these comparisons (data not shown).
No statistically significant differences emerged from these comparisons (data not shown).
However, when less strict eQTL thresholds are used, we could observe a similar genomic distribution of CEF for correct and shuffled comparisons (data not shown).
