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In this study, we performed a systematic exploration of the somatic mutations and their related features for cancer classification using a machine learning approach and the most comprehensive somatic mutation dataset so far.
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The exercise was only ever intended as a bit of fun with a large and interesting dataset, so there really shouldn't be any problem here.
"We've really only sort of looked at half the dataset so far.
He eventually sent me the original dataset so that we could run our reanalysis.
Most alternative lenders don't have access to the special dataset so they are missing some critical payment records there.
This provided a uniquely high-resolution mutation dataset for mapping LD.
Here, our algorithm outperforms SIFT, PolyPhen-2 and Mutation Assessor across all mutation datasets.
We devised these proxy datasets so that the positive subset of mutations is enriched in likely driver mutations - either because they have been manually curated from previous reports, because they occur in known cancer genes, or because they appear recurrently in the dataset - and is complemented by a negative subset of mutations enriched in passenger mutations.
This leads to improper weighting of the imputed datasets so that one or two datasets take approximately all the weight.
The protective mutation is so rare that only one in ten thousand people carry it.
You'd made mutations look so good".
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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