Sentence examples for methods on datasets from inspiring English sources

We assess the performance of the CMAT and other pooling methods on datasets simulated with population genetic models to contain realistic levels of neutral variation.

Our experiments show that Fiona outperforms other methods on datasets from different sequencing technologies.

ChromSDE performs better than other MDS-based methods on datasets with a low SNR, corresponding to datasets with low coverage and, consequently, many non-interacting pairs of beads.

To study the performance of the methods on datasets with very high rates of DTL, we created gene trees with very high rates of DTL on the 50-taxon species trees from the basic simulation setup (see Supplementary Table S1).

To assess this phenomenon, and evaluate the robustness of the different methods (including NMDS and PM2, which automatically infer a transfer function), we now study the performance of the methods on datasets generated with varying α parameters.

We explain the superiority of our methods on datasets that do not even match the data generation model as follows: (i) they construct biclusters simultaneously, thereby, taking overlaps into account; (ii) the decorrelation of factors minimizes redundancy of biclusters; (iii) the low complexity of the model ensures low parameter interdependencies, which facilitates model selection.

The proposed method is shown to have better performance than other methods on Dataset-B.

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

We compare the accuracy of alignment methods existing at the time of this work and the new alignment method on datasets simulated to encompass a broad range of genomic mutation types and rates, including inversion, gene gain, loss, and duplication.

For comparison, we tested the proposed method on datasets integrating only two kinds of data, i.e., somatic mutations and CNVs or expressions.

We have also evaluated the performance of QED method on datasets used in this study, QED correctly classified 44.8% approved and 81.28% experimental drugs from the training dataset and 40% approved and 52.5% experimental drugs from the independent dataset.