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We first test our methodology with a numerical benchmark using a synthetic model.
Additionally, we benchmarked the fingerprint encodings on the large-scale Ames toxicity benchmark using a large-scale linear support vector machine.
PyNAST was as fast as mothur in the benchmark using a reduced width alignment and matched SINA when using the full 51 000 column MSA.
Although CMP's performance was disappointing in our original benchmark using a set of nine oligogenic diseases with Mendelian inheritance [ 12], it produced a surprising number of statistically significant results when confronted with the GWAS data on seven complex diseases [ 9].
To better understand the potential benefits of incorporating a cancer-specific weighting scheme into our algorithm, we compared the score/prediction assignments for all mutations in the Capriotti and Altman (2011) benchmark using a cancer-specific weighting scheme with the score/prediction assignments for the same mutations using our original inherited-disease weighting scheme.
Therefore, to derive a prediction threshold capable of being applied under all conditions, we plotted the distribution of the predicted magnitude of effect for all mutations in the Capriotti and Altman benchmark using a leave-one-out cross-validation procedure (Fig. 1).
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com