Sentence examples for dataset method from inspiring English sources

Table 6 2-classes experiments results with 4 datasets Dataset Method Accur.

Table 1 Summary of mean errors of both methods on both datasets Dataset Method Transl.err.err

Therefore, in a second experiment, we again examined effects of self-selection on first impressions, but using a more direct test: comparing trait judgments to images that had been explicitly chosen as most and least likely to be used as profile images for different network contexts (see "Profile Image Dataset" method).

Table 3 Recognition accuracy of various approaches on the Youtube-LQ dataset Method YouTube-LQ Method YouTube-LQ STIP (baseline) 67.57 iDT (Baseline) 74.04 STIP+LBP-TOP 70.69 iDT+LBP-TOP 75.59 STIP+LPQ-TOP 69.13 iDT+LPQ-TOP 76.02 STIP+BSIF-TOP 76.05 iDT+BSIF-TOP 80.45 STIP+deep object [35] 85.37 iDT+deep object [35] 85.36.

Results are presented for four combinations of method and dataset: method A, B, and C applied to dataset 1, and method C applied to dataset 2. For classes of animals with traceability above 0.50, there was a clear decrease in the imputation error rate with increasing traceability.

Such studies will be greatly facilitated by the dataset, methods, and analysis pipeline provided in this study.

We further divided highly expressed genes into stably expressed (108 genes) and variably expressed (168 genes), based on the 10 pg dataset (Methods).

The median ratio of intronic densities to their corresponding exonic densities was found to be 11.95% across all samples in the "Total Intron-Exon Pair-Wise Dataset" (Methods).

The Mc.TGD significantly advances from the TGDR by being able to analyze multiple datasets, whereas the TGDR is a single-dataset method.

This is actually a desirable characteristic of the multi-dataset method proposed in this paper, because low-powered or under-powered datasets will automatically have less influence on the final combined results.

In conclusion, the encouraging results obtained in the present study indicate that the multi-dataset method we proposed is an effective and robust way to generate high quality gene signatures for connectivity mapping, and the publication of the gene signature and drug hits obtained here will be beneficial to systems biology, cancer bioinformatics research communities and beyond.