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In an independent microarray data set we found that these chromatin modifier genes were also able to discriminate samples according to tissue type, although the degree of discrimination was much smaller.
Newmaster et al. [20] analyzed over 10,000 rbcL sequences from GenBank and found that rbcL could discriminate samples in approximately 85% of pairwise comparisons of congeneric species, whereas the discrimination efficiency was about 88% when a combination of psbA-trnH and rbcL was used across 96 diverse species of 48 genera from 43 families [21].
It is well known that classification techniques are more powerful in decision making, when they use a sufficient number of features to discriminate samples.
If one uses whole rock samples, it is necessary to discriminate samples whose magnetic signals are dominated by secondary magnetic minerals.
There are bands which are specific of some accessions and can be used as markers to discriminate samples at interspecific level.
To further demonstrate that age-related DNAm patterns were independently associated with tumor presence, we asked if the age associated CpGs derived from the 148 healthy controls (293 CpGs with FDR<0.3) [16] were able to discriminate samples according to disease status (Figure 4a).
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Households were selected using the discriminate sampling technique (Strauss and Corbin 1998), and interviews were conducted in Kiswahili with the head of the household as described by Masozera (2010).
We failed to conduct relational and variational sampling, and discriminate sampling.
Microarray has become an important tool for identifying genes that discriminate sample classes because of its power of monitoring the expression levels of thousands of genes in a single experiment.
The first axis discriminated samples from the IR64 irrigated treatment, the second discriminated IR64 drought samples from the two Apo treatment classes, while the third described those compounds that differed between the two treatments applied to Apo.
The discriminative score of a subnetwork which reflects how well the subnetwork discriminates samples of different phenotypes (or classes)–was derived from mutual information between subnetwork activity and the phenotype.
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