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We identified out of 489 clusters, 433 with a THR above 0.4 corresponding to 5977 probes (86% of the probes).
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By investigating the circuits in the context of lung specific genes, we identify out the regulatory combinations for lung specific genes, as well as for those lung non-specific genes.
We identified 4 out of 68 chemicals that were used to train the NPN model but the oracle model associated them with the PN model (see Figure 6).
For example, we can identify out-layers and potential weak signals of change by QCA methodology.
We identified an out-of-frame deletion in DMD exons 45-52 in both DMD-iPS1 and DMD15 iPSCs (Fig. 1B, supplementary material Fig. S1b).
By considering those circuits associated with lung specific genes, we have identified out the dynamic regulatory interaction of miRNA-TF-mRNA circuits in different lung development stages.
The two putative null alleles of this type that we have identified (out of 71 mutations) represent a frequency not significantly different from the expected five percent.
While a number of perturbations have been shown to alter spindle size, we have identified, out the hundreds of known spindle associated proteins, physiological effectors that operate to scale the spindle during embryogenesis.
Using in-gel digest and MS, we identified 13 out of these 21 proteins.
In conjunction with isotope pattern analysis, we identified 10 out of 12 molecular formulas, and SIRIUS was selected "best automated tool" of the molecular formula challenge [50].
The same analysis were repeated for the training dataset of the PN model and we identified 9 out of 136 chemicals that were associated with the NPN model by the oracle model (see Figure 7).
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