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DNA microarrays are able to detect microbial sequences from any sample in a parallel and very fast, high throughput way.
Microarrays are able to measure gene expression changes, but are limited by the availability of Expressed Sequence Tags (ESTs).
These results indicate that our microarrays are able to detect both low and high fold-changes with high accuracy in different experimental conditions (p-value < 0.01) (Table 2).
Microarrays are able to tolerate DNA of lower quantity and quality typical of patient-derived field samples, which contain far more human than parasite DNA.
Others (e.g. PFGE, MLST, AFLP, VNTR, fla sequencing, DNA microarrays) are able to discriminate between strains [ 8, 14, 29, 32- 34].
While SNP-based microarrays are able to detect polyploidies and uniparental disomies, purely CGH-based platforms (like the BAC-based used in this study) are not capable of identifying such events.
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In addition, the microarrays were able to discriminate between closely related viral strains in a number of cases.
We used our platform to see whether the flax microarrays were able to detect differentially-expressed genes between 2 contrasting flax genotypes - Drakkar and Belinka.
In addition, the chicken microarrays were able to identify a number of key immune genes that were regulated in the H5N1 infected ducks.
This result shows that pathway-level analysis of microarrays is able to provide a functionally coherent and highly efficient prognosis classifier, which will most probably be more stable than the classifiers from which it originates.
Drug sensitivity assays, combined with DNA barcode microarrays, were able to reveal genomic profiles for both the drug's targets through Haplo Insufficiency Profiling (HIP) and pathways that buffer the drug target pathway through HOmozygous deletion Profiling (HOP) [ 8, 9].
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