Sentence examples for individual analysis methods from inspiring English sources

Since workflow environments such as KNIME are enjoying growing popularity in cheminformatics, we plan to integrate individual analysis methods and visualization techniques of Scaffold Hunter into the KNIME platform in the future.

Lastly, a challenge for current studies is a lack of group and individual analysis methods that can be reliably applied to capture and quantify factors that contribute to variability introduced by developmental and disease processes.

Since villages (clusters) were our unit of sampling, individual analysis methods were not appropriate due to artificially small standard errors.

The individual analysis methods are described in Table 2, including differences in the definition of cutoff values for parameters such as fold change or p-value.

In the present study we applied an individual analysis method of high resolution MRI images to account for the differences of peripheral cortical structures such as the HG.

Due to the hypothesis-driven approach, individual analysis method (not group averages) and homogenous data we are confident that the number is sufficient to validate our results.

Very recently Martens et al. employed an individual analysis method and found significant volume increase of the left PT in a subgroup of WS subjects who demonstrated specific musical strengths [15]; but this finding remained unexplained so far.

The results are easier to interpret biologically, and the method is more accurate and robust than individual gene analysis methods, such as fold change analysis of expression levels (Subramanian et al, 2005).

The six individual level analysis methods were: i) standard logistic regression, ii) robust standard errors approach, iii) generalized estimating equations, iv) random-effects meta-analytic approach, v) random-effects logistic regression, and vi) Bayesian random-effects regression.

In this paper they compared three cluster-level methods (un-weighted linear regression, weighted linear regression and random-effects meta-regression) to six individual level analysis methods (standard logistic regression, robust standard errors approach, GEE, random effects meta-analytic approach, random-effects logistic regression and Bayesian random-effects regression).

The analysis of CRTs may be based on the analysis of aggregated data from each cluster or based on individual level data, which correspond to the cluster-level and the individual-level analysis methods, respectively.