By associating a dataset with repeatedly measured risk factors and a dataset with genetic markers, we can extract the common features out of the two sets.
First, we extracted markers representing independent genes from the DNA markers used for the anchoring BAC clones.
If concentrations of immune markers were not reported, we extracted the effect size from exact p-values and sample size (Dahl et al., 2014; Euteneuer et al., 2011; Hornig et al., 1998; Li et al., 2013).
With the GMM method, we extracted significant marker combinations consisting of one to three interacting markers.
To study the variations in markers associated with cognitive functions, we extracted three event-related field (ERF) features from time-locked global field power (GFP) epochs using MEG while participants were performing a numerical N-back task in four consecutive measurements conducted during two different days separated by two weeks.
We extracted a subset of markers from the LKH maps for which the ordering was strongly supported by RH data.
In an effort to better characterise the molecular implications of TMPRSS2 ERG fusions in prostate cancer and to identify other genetic prognostic markers, we have utilised RNA extracted from radical prostatectomy specimens in a prospective cohort previously characterised for TMPRSS2 ERG T1/E4 fusion transcript expression by qualitative PCR (Nam et al, 2007).
Based on both features, internal markers are extracted by a CFAR detector and external markers are extracted by a power ratio (PR) detector.
In the first frame, markers are extracted which provide reliable seed regions for segmentation in subsequent frames.
Potential markers were extracted from VIP plots constructed following the OPLS analysis, and markers were chosen based on their contribution to the variation and correlation within the dataset (Fig. 2d).
