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It is interesting to note that the speech recognition community actually began work on training NNs with soft posterior probabilities instead of hard targets generated from forced alignment a long time ago [17 20].
To confirm discovery findings, immunoblot confirmation analysis was performed using two sets of animals, independent from the discovery experiments (Experiments 3 & 4, Table 1) on selected targets generated from each discovery method.
To extend our gene expression profiling analysis to adult muscle, we used cRNA targets generated from the gastrocnemius muscle RNA of wild-type and Myog-deleted mice for hybridization to Affymetrix microarrays.
However, the 3 aRNA targets generated from brain or ovary were similar [see Additional file 2C].
As a negative control, three arrays will be hybridized to cRNA targets generated from the background human sample without any external RNA controls.
As a negative control, three arrays will be hybridized to cRNA targets generated from at least one representative background human sample in the absence of any external RNA controls.
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Target generated from each aliquot was hybridized to an array, generating single (NT) and replicate (GFP+ and GFP+/-) datasets.
The failure to disclose specific performance targets generated the most comments from regulators.
The center frames of both inputs are from state "1238," which would be the training label for both inputs if hard targets were generated from a forced alignment of a reference transcription.
In those experiments, cDNA targets were generated from The RNA Spikes, a commercially available set of 8 purified Escherichia Coli RNA transcripts purchased from Ambion Inc. Lengths of the RNA sequences in the set are (750, 752, 1000, 1000, 1034, 1250, 1475, 2000), respectively.
cDNAs of miRNAs and targets were generated from 2 μg of total RNAs of 24 M.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com