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The benchmark dataset can be used in the future to evaluate novel methods for de novo motif discovery, enhancer prediction, or target gene prioritization.
The benchmark dataset can be freely downloaded from http://lin.uestc.edu.cn/server/iJPred/data.uestc.edu.cn/server/iJPred/data
A complete list of all the PDB codes and sequence for the benchmark dataset can be found in Supporting Information S1.
The presented inference algorithms and the 5000-gene benchmark (the Pula-Magdeburg single-gene knockout benchmark dataset) can be downloaded from http://sysgensim.sourceforge.net/datasets.html.html
A complete list of all the codes and sequence for the expanded benchmark dataset can be found in Supplementary Material S2.
A complete list of all the codes and sequence for the benchmark dataset can be found in Supplementary Material S1, available online at http://dx.doi.org/10.1155/2014/294279.org/10.1155/2014/294279
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Benchmark datasets can be designed at various levels of the concept hierarchy using a simple graph-theoretic distance.
Our results also demonstrate that conclusions of superior performance of one method relative to another that are based on estimates of performance obtained using UPDS versions of MHC-II benchmark datasets can be misleading.
Since independent dataset can be treated as a special case of subsampling test, one benchmark dataset is sufficient to serve all the three kinds of cross-validation.
Since independent dataset can be treated as a special case of sub-sampling test, one benchmark dataset is sufficient to serve all the three kinds of cross-validation.
The complete dataset can be downloaded here.
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