Sentence examples for benchmark ppi from inspiring English sources

We use Jaccard coefficient, recall, and precision to measure the agreement between a benchmark PPI dataset and a testing PPI dataset.

Recall is the proportion of benchmark PPIs that are in the testing dataset.

We have compared these techniques over two publicly available benchmark yeast PPI datasets, namely DIP data [ 93] and Krogan data [ 33].

The prediction was proven highly accurate on the benchmark of yeast PPIs datasets.

When switching benchmark from the H37Rv B2H PPI dataset to the H37Rv STRING predicted functional associations dataset (with score ≥770), the resulting Jaccard coefficient, precision, recall, and overlapping PPIs number, increase significantly (about two orders of magnitude).

Nevertheless, the quality of tentative physical interactors needs to be carefully validated, including verification by reciprocal tagging and purification, benchmarking with manually curated PPI databases such as DIP, BIND and IntAct, correlation with gene coexpression, elevated cooccurrence of orthologs in other species and other evidences of functional relatedness.

Twelve PPIs in the benchmark set contain no negative examples because the ligand protein of the PPI is a short peptide where all, or nearly all, of the interface residues make up a SMISP.

In most cases, this results in positive examples that are identical to the benchmark SMISPs, but five training set PPIs have large benchmark SMISPs that are decomposed into all possible maximal subsets of residues that fit within a 12Å distance cutoff.

The H37Rv B2H PPI dataset is used as benchmark and different subsets of H37Rv STRING PPIs are tested against it.

Precision is the proportion of testing PPIs that are in the benchmark dataset.

Physician PPI prescribing patterns may also be benchmarked against each other with financial penalties for excessive costs [ 24].