Sentence examples for datasets combinations from inspiring English sources

From common polymorphisms data of gene-chip vs. in-silico SFPs comparison study of japonica&indica datasets combinations (Table 6b&c), we estimated further the frequency of overlapping SFPs by comparing their common SFPs.

Although common SFPs were also observed between diverse CP&LG and CP&RT (Table 5a) and also between CP&LG and LG&RT (Table 5c) datasets combinations respectively and expectedly the number of overlapping SFPs were much less.

The chi-square test for independence for all three datasets combinations (Table 5) were highly significant showing strong association among the three pairwise comparison The availability of genome sequence information of Nipponbare [17] and 93-11 [18] allowed us to predict in-silico SFP candidates between above genetically diverged japonica and indica sub-species of rice respectively.

We accepted all individuals as hybrids that were identified in at least one of two corresponding datasets (combinations A and B), by at least one program (combinations C and D) and by at least one program in at least one of two corresponding datasets (combination E).

In gene-chip predicted SFPs comparison study in japonica&indica datasets combination ∼>70% of the total polymorphic SFP were common (Table 5b).

This contrast with ∼30% of common SFPs observed in gene-chip vs. in-silico studies in two different japonica&indica datasets combination (Table 6b&c).

The higher number of common SFPs in gene-chip predicted SFPs of japonica&indica datasets combination (Table 5b) may be due to indica variety (RT) was common in both the datasets and secondly genetic divergence between two japonica varieties viz.

The significantly higher and comparable number of common polymorphism in two different japonica&indica datasets combination (Table 6b&c) reconfirms our earlier observation of occurrence of common variation between japonica&indica subspecies of rice (Table 5b).

We use a method from Bayesian statistics, the Weights of Evidence model, and produce ten predictive maps of wildfire risk: (1) five maps for a two-month fire season combining datasets of evidence variables and (2) five maps for the four-month fire season using the same dataset combinations.

Two sets of public dataset combinations were used to build and evaluate the PLSA model.

For each test and retest scan of the TRT dataset, (combinations of) the above-mentioned adapted versions of the KF method were applied to automatically segment the tumour.