Your English writing platform
Discover LudwigExact(3)
Table 1 shows figures of number and average size of known complexes derived from the two testing PPI networks.
We use Jaccard coefficient, recall, and precision to measure the agreement between a benchmark PPI dataset and a testing PPI dataset.
We believe, however, emphasis should be placed on testing PPI in which at least one protein is not conserved between species, because while these may be easily predicted, PPI between proteins with no orthology can never be predicted in this manner.
Similar(57)
Precision is the proportion of testing PPIs that are in the benchmark dataset.
We went on to test PPI and found no differences between wild-type and transgenic mice at any time point in either sex-averaged or sex-separated data.
A test PPI network is derived from a set of test genes by selecting all the interactions from the general PPI network occurring between proteins encoded by the genes in that test set (Equation 1).
PPI in males and D1 females was significantly affected by the time of day of testing with PPI being reduced in the afternoon and evening compared to morning.
Compared to the more common high-throughput yeast two-hybrid assay, CAPPIA offers the advantage of testing mammalian PPI within a cellular context that more closely mimics the native protein environment.
To test for PPI effects across participants at the group level (independent of trait anxiety), the single-subject contrast images testing for an effect of the PPI regressor were entered into a second-level random effects analysis for a t test.
Animals were tested for PPI of the auditory startle response (ASR) (ISIs of 0, 8, 40, 80, 120, and 4000 ms, six trial blocks, Latin-square design) on days 30, 60 and 90.
Two groups of male schizophrenic patients: (i) stable on a range of typical antipsychotics (n=20), and (ii) stable on risperidone (n=10) were tested for PPI (prepulse-to-pulse intervals: 30, 60, and 120 ms, prepulses 15 dB above the background) of the acoustic startle response, and compared with a group of healthy male subjects (n=20).
Write better and faster with AI suggestions while staying true to your unique style.
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