Sentence examples for discriminatory models from inspiring English sources

In conclusion, these ex vivo lung slice cultures are discriminatory models for the study of respiratory virus infections and can be used to inform the design of future in vivo experiments, thereby reducing numbers of animals required.

No company is knowingly creating biased AI, of course — all these discriminatory models probably worked as expected in controlled environments.

To assess the differences of the obtained discriminatory models, likelihood ratio tests were performed.

However, it has proven to be an excellent marker of M.tb infection and this is consistent with the inclusion of ESAT-6/CFP-10fp into all the best discriminatory models.

The discriminatory models built from the 13 expression based biomarkers combined with the plasma protein biomarkers proved to be significantly better than the models built from the plasma protein biomarkers alone (p < 0.0001, likelihood ratio test).

The measure of allelic loss of heterozygosity combined with tumor number, tumor size, vascular invasion, lobar distribution, and patient gender provide a highly discriminatory model for predicting cancer recurrence after liver transplantation.

The stability of the proposed discriminatory model related to the DS score was tested by k-fold cross-validations, where k ranged from 3 to 10.

Although discrimination of colitic from non-colitic mice by non-invasive factors is ideal, as a proof of principle for discriminatory modeling and to gain biological insight into the mucosal disease process tissue PLS-DA models were generated using the colon levels of the factors.

In this report, all experiments described hereafter were performed with a discriminatory model trained using all data from Datasets L and M. Because the proposed method is the first DS-specific detection and alignment approach, the quality of its structural alignments may serve as a standard for the evaluation of related future works.

According to Figure S2, the classification quality of the discriminatory model remained high and stable in both datasets, even though Dataset L was much smaller than Dataset M. Combining these performance data obtained by both the inter-dataset (Table S3) and intra-dataset (Figure S2) training and testing, the feasibility and robustness of the proposed DS detection methods was confirmed again.

We refer to this discriminatory model as BALAD-2d.