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Our algorithm predicts surprising differences between the three- and four-dot experiments.
It must also be noted that our algorithm predicts a D-trajectory of a person for a day, given a history of D-trajectories of the person; the history is a set of D-trajectories, one D-trajectory for each day in the person's history.
Given a cohort of hybridizations, our algorithm predicts, for the single reading, the probability of each gene's being in an Up or a Down state in each hybridization.
Since our algorithm predicts connections biased by the biology of the system under study (through the use of gene expression data from ApcMin/+ mouse intestinal tissue), a particular protein or edge may not appear in the network if the pathway (i.e. chain of proteins) on which it resides does not meet the gene coexpression and/or GO association rule thresholds.
In this case, our algorithm predicts that its functionality of binding depends on six other TFs.
Our algorithm predicts a higher number of clusters, being all of them statistically significant.
Similar(51)
Our algorithm predicted 1578 insertions, 2615 deletions, and 373 inversions between the reference NCBI human genome and the JCVI donor.
For external lactate concentrations up to a critical value of 12.4 mM our algorithm predicted a thermodynamically realizable flux distribution.
Our algorithm predicted a core TRR signal within the overlapping region with maximum TRR scores around 35.
In turn, we also tested the list of false positives, for which our algorithm predicted 33 genes to be essential, in contrast to the experimental high throughput screen.
Our algorithm predicted the presence of 18 distinct clusters, consistent with the actual content of the simulated draft assembly and experimental Hi-C data.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com