Sentence examples for the human datasets from inspiring English sources

For the human datasets we used the 'regulatory circuits' generated by Marbach et al.41 in which regulators were linked with target genes through a series of steps starting from binding motifs in active enhancers using FANTOM5 project data.

More specifically, 1/6 of the human datasets was selected as independent test datasets and the remaining human datasets selected as training datasets.

The characteristics of the human datasets analyzed [8], [9], [18] [22] are summarized in Table 1.

Table 2 displays the statistics defined in the Results section, subsection "Module Assessment", that were achieved by the comparison partners on the human datasets (see Methods section, subsection "Data").

Seven individual genes represented in the human datasets were of particular interest: KLK3 (which encodes prostate-specific antigen, or PSA), MYC, SRC (which encodes c-Src), ERBB2, EGFR, CCND1, and AKT1.

For mouse data (SRR689233), for example, the compression is on par with that achieved for the human datasets.

Extensive experiments and comparisons on the face dataset and the human dataset show the proposed approach outperforms the traditional single-feature learner and other multi-feature learners in discriminative and generalization abilities.

The top-ranked motif for the human dataset was ̂.A.G, which occurred in 28 unrelated proteins.

For the human dataset, probes available on both platforms were selected for further analysis.

Last, note that Matisse achieves top-rated ER values also on the human dataset.

Scores in HMMSplicer's predicted junctions aid the discovery process, as evidenced by the XBP1 example in the human dataset.