Sentence examples for more different datasets from inspiring English sources

Given this criterion for assessing strong support, "congruence" would represent cases where a particular clade is strongly supported in two or more different datasets, while "conflict" would represent cases where a taxon or clade is strongly supported in different positions in two or more datasets.

A total of 9781 genes were identified in four or more different datasets.

The precise levels of helix/strand content and length differed indeed more between different datasets (PDB subset versus entire set of human proteins) than between observation and prediction for any set for which we have experimental information.

More than three hundred different datasets were performed and corresponding cuttings bed thickness values were obtained.

We observe that (i) different learning method combinations obtain the similar MAP and MaRR performances on all four datasets and 2L-Best1 performoreore stably across different datasets than the other combinations.

As we have demonstrated in this paper, the LLR-based pathway activity inference scheme significantly improves the discriminative power of the pathway markers, increases the overall classification accuracy, and finds reliable pathway markers that are more reproducible across different datasets.

We apply MSpresso to data from different MS/MS instruments, experimental conditions and organisms (Escherichia coli, human), and predict 19 63% more proteins across the different datasets.

We normalized the node degree in order to make the different datasets more comparable since the average degree is dependent on the number of interactions and nodes identified by the method.

We believe that this discrepancy is more likely due to different datasets than to different classification methods because when a classification method similar to that used in ref. [ 17] was applied to the current dataset, a stronger phase-0 bias in lineage-specific introns was obtained [ 18].

Ensuring consistency among the different datasets permits more meaningful derivation of vulnerability and fragility functions.

Combining information from different datasets allows more in-depth analysis, such as using train location data to enhance the analysis of speed profiles and energy consumption.