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Due to the obscurity of the SFPs, it is hard to exclude that the identified sweep regions may in fact represent multiple independent sweeps in the same region.
Considering the wide range of potential applications of detected SFPs, it is worthwhile to investigate the distribution of SFPs in the genome.
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Noting that when applying (1.2) and (1.3) to solve the practical problems such as signal processing and image reconstruction, which can be covered by the SFP, it is hard to avoid that a fixed stepsize related to the norm of A sometimes affects convergence of the algorithms.
Byrne's CQ algorithm is an effective algorithm to solve the SFP, but it needs to compute (|A|), and sometimes (|A| ) is difficult to work out.
However, because the number of sequenced targets was biased to SFP-predicted ones, it was estimated that the sensitivity would be higher than the true value.
Nevertheless SFPs have a clear advantage when it comes to specifically mapping genes.
As reported by Fujisawa et al., it is difficult to detect SFPs at low expression levels [ 26].
We stress that the method used here to identify SFPs is conservative, in the sense that it underestimates the total number of proteins present in the seminal fluid, for five reasons.
SFP technology draws its strength from the fact that it can be implemented in a variety of genetic applications, such as marker discovery and fine mapping of traits, as well as for genome-wide association studies [ 3, 6, 7].
Although we observed considerable increase in detection sensitivity of SFPs at less stringent estimated FDR (Table 3), it was also accompanied by a significant increase in the observed FDR determined after sequencing compared to the estimated value by permutation.
These differences made it difficult for SNEP to detect SFPs, because SNEP detects SFPs as outliers.
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