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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.
To test whether our benchmark results were cell-type dependent, we ran the same peak detection method on datasets from the HeLa-S3 cell line for TFs c-Myc, c-Fos, E2F4, and Max and created additional site datasets on which we ran all of the methods.
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.
We applied our method on datasets that measured gene expression after exposure to vitamin D, tetrachlorodibenzodioxin (TCDD), bisphenol A, zinc, and estradiol (2 datasets) on different tissue types (Additional File 1, Supplementary Table S1).
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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 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.
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).
Table 1 Summary of mean errors of both methods on both datasets Dataset Method Transl.err.err
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