Sentence examples for dataset on the identification from inspiring English sources
First, a long-interval identification window is selected, and the dataset on the identification window is used to estimate a DTMS model, which will be used to do asset allocation on the following short-term trading interval that is referred to as the trading window.
The dataset on the identification of PPARα target genes in mouse liver has a 2 × 2 factorial design; that is factor 'treatment' has 2 levels (WY, Control), as has the factor 'genotype' (wild type, knockout).
The application of our method to this dataset resulted on the identification of POU5F1, SOX2, and MYC as RDs among other alternative combinations.
After asset allocation is over on the trading window, the length-fixed identification window is then moved to a new window that includes the previous trading window, and a new DTMS model is estimated by using the dataset on the new identification window.
Different bioinformatics tools were used for analysis of the large dataset with emphasis on the identification of pathways differentially regulated between the Frzb-/ and wild-type mice.
In view of the fact that performance in keystroke dynamics is highly dependent on the dataset, the identification of benchmarking datasets turns out to be fundamental.
In early 2001, Ding and Dubchak built the 27-class folds dataset and started research on the identification of 27-class protein folds with multiple feature groups.
Even more, the dysregulated pathways detected by well-known GSEA [1] on these datasets shown weak performance on the identification of pathways full of DEVGs (Fig. 5c).
Mapping the sequences of the small RNA dataset on the precursor sequences led to the identification of miRNA*for all eight conserved families (HbmiR156, HbmiR159, HbmiR166, HbmiR319, HbmiR396, HbmiR408, HbmiR476 and HbmiR2910) and for the five putatively new families HbmiRn3, HbmiRn4, HbmiRn8, HbmiRn9 and HbmiRn10.
Analysis of these datasets has tended to focus on the identification of markers able to divide disease samples into prognostically relevant classifications [ 1- 5].
