This meant over 5000 classifiers were built for this study so that we could find an optimal Weka misclassification cost setting for a specific base classifier when applied to a specific type of dataset.
Users can select a type of protein interaction dataset and enter names of genes as an Entrez gene ID, gene symbol, GenBank accession number for nucleotide and proteins, or NIA Mouse Gene Index U cluster IDs.
Compared with similar published methods, this method is applied to a very different type of dataset.
The combination of multiple datasets, as illustrated here, can further improve the functional inferences that typically are generated via a single type of datasets.
A recently published example of this type of dataset is the DREAM Drug Sensitivity Challenge (subchallenge 2) [21], in which all combinations of 14 compounds were tested on the LY3 lymphoma cell line.
The final type of dataset was added as a member of 10.14469/hpc/1116 and is described in more detail below as LiveView below.
We come to the conclusion below that the benchmarking approach with this type of dataset is not useful for training a system that must learn invariant object representations.
The third and final type of dataset, called the "station demo set," was used for a practical demonstration at the station site.
But the ultimate value to the industry is also apparent, and it's rare to come across this size and type of dataset in the wild.
The second type of dataset, called "tracking set," was also taken in the laboratory site.
