For this, we used the Wilcoxon Rank Sum Test [27] as implemented in R (R version 2.
Sample text:```The merging is based on the increase in the weighted-bycounts entropy [1] and is stopped by a threshold indicating the minimum number of training samples required to estimate a weight distribution.```
Sample intent: method
Sample text:```The area under the curve represents the probability that the logistic regression model will rank a randomly chosen positive instance (i.e., an error) higher than a randomly chosen negative instance (i.e., nonerror; Fawcett, 2006; Macmillan & Creelman, 2005).```
Sample intent: method



Sample text:```The merging is based on the increase in the weighted-bycounts entropy [1] and is stopped by a threshold indicating the minimum number of training samples required to estimate a weight distribution.```
Sample intent: method



Sample text:```Estimation of overall mortality ratio (relative risk) was carried out using the random e ects model according to DerSimonian and Laird [9].```
Sample intent: background



Sample text:``` The GRS was then calculated by summing across all SNPs (the GRS could theoretically range from 0 to 58).```
Sample intent: background



Sample text:```The methods working directly on the count data can be broadly divided into parametric (baySeq [14], EBSeq [24], ShrinkSeq [27], edgeR [23], DESeq [7], NBPSeq [15] and TSPM [13]) and non-parametric methods (NOISeq [25] and SAMseq [26]).```
Sample intent: background
Sample text:```The items are internally consistent and reproducible and the questionnaire may therefore be considered to have at least as favourable levels of reliability as clinical scores used to assess outcomes (Koran 1975).```
Sample intent: result



Sample text:```While this method is known to be generally reliable, there are some questions about the representativeness of the data used [25].```
Sample intent: background



Sample text:```Results from a RCT assessing in-person post-test genomic counseling on 199 OSU-CMPC participants were published previously [18].```
Sample intent: method



Sample text:```Note that in their pioneering paper Calvet and Fisher (2002) proposed the simplified version of the construction of Mannersalo et al (2002).```
Sample intent: method



Sample text:``` The GRS was then calculated by summing across all SNPs (the GRS could theoretically range from 0 to 58).```
Sample intent: background



Sample text:```Our results confi rm previous studies that reached the same conclusion (Boushey et al 1994; Vermeulen et al 2000; Till et al 2005; Marcucci et al 2005).```
Sample intent: result



Sample text:```This is interesting in relation to the result from our previous CGH study (comparing the number of significantly hybridizing probes on the microarray) that showed high sequence divergence on the Z chromosome between the common whitethroat and the zebra finch [62]; and such patterns are also found in other species [48,51,69].```
Sample intent: result



Sample text:```This seems to be the main drawback of the current implementation in comparison to the method of Black Jepson (“refined” in Table 1).```
Sample intent: result



Sample text:```The area under the curve represents the probability that the logistic regression model will rank a randomly chosen positive instance (i.e., an error) higher than a randomly chosen negative instance (i.e., nonerror; Fawcett, 2006; Macmillan & Creelman, 2005).```
Sample intent: method



Sample text:```rural, we calculated the relative prevalence of a missing value by other demographic and fitness variables using logistic regression [49].```
Sample intent: background