Sentence examples for impressive datasets from inspiring English sources

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Indeed, efforts by the MitoCheck consortium have generated impressive datasets of physical interactions within the mammalian chromosome segregation pathway and siRNAs that induce various mitotic defects in human cells (Hutchins et al. 2010; Neumann et al. 2010).

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It had the most impressive dataset the world had ever seen; the most sophisticated algorithm to make sense of it; an audience of a billion users expressing their interest; and more than a million advertisers bidding furiously to reach those consumers at just the right moment.

The authors Jeschke et al. (2015) have collected an impressive dataset describing pathophysiological reactions to burns in elderly versus adult burn patients.

Nevertheless, there were two principal suggestions how the manuscript could be improved: Despite their impressive dataset, you identify only six examples for allele-specific imprinting, only three of which are investigated in detail.

The manuscript has been improved but there are some remaining issues that need to be addressed before acceptance, as outlined below: You use an impressive dataset of gene expression, DNA methylation and small RNAs from seeds of reciprocal hybrid plants between two Arabidopsis ecotypes to determine how many genes are imprinted in Arabidopsis.

The total number of black patients in this clinical trial dataset is impressive given the small amount of published information on the potential effect of race on treatment outcomes in schizophrenia.

Although the difference in overall effect sizes for different breast cancer subtypes were less impressive in the validation dataset, the strongest protective effect was similarly observed for the ER-negative subtype (OR> 5 cups/day: ≤ 1 cup/day: 0.67 (0.43, 1.05), P trend = 0.326), followed by the PR-negative subtype (OR> 5 cups/day: ≤ 1 cup/day: 0.70 (0.49, 1.00), P trend = 0.280).

BvSB-ADN also showed impressive performance on the MNIST dataset.

However, when the image was refined, the three dimensional image was found to produce an impressive representation of the computational dataset.

We train this network using a high-end graphics processor unit (GPU) on the publicly available Kaggle dataset and demonstrate impressive results, particularly for a high-level classification task.

However, for image and video data, it is still hard to map the visual features to the semantic labels, although many maximum margin techniques have announced impressive classification results in their own datasets.

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