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Compound descriptor array based methods use machine learning approaches to relate compound interactions with arrays of observations regarding a compound.
Future array based methods could include mRNA profiling and a separate array for profiling the genotype of the corresponding probe sets and their surrounding regions.
Furthermore, genotyping methods are relatively inexpensive, robust and rapid, and thus would likely be significantly less expensive than expression array based methods.
Compared to array based methods, MLPA, with subsequent validation of potential deletions, is a relatively practical, inexpensive and fast tool for screening for chromosome rearrangements in ASDs.
Suffix tree and suffix array based methods build a data structure from the input reads, and replace a substring in a read if its number of occurrences falls below that expected given a probabilistic model.
The advantage of this approach is seen especially in and near highly duplicated genomic regions, such as segmental duplications (SDs) where most of the array based methods fail [ 27, 28].
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The use of different non-array based methods for quantification of gene expression, such as reverse transcription polymerase chain reaction (RT-PCR) is also desirable.
Furthermore, although the price is continuously dropping, the cost of sequencing at present is still higher than array-based methods.
As expected, array-based methods generated fewer but larger CNVs, whereas NGS based methods generated more but, on the average, smaller CNVs.
Copy number analysis by sequencing (both by high or low coverage) offers substantial benefits over array-based methods, including higher resolution (down to a single base) and precise delineation of breakpoints [ 72, 73].
More recently, several types of computational methods based on NGS data have been proposed for finding SVs with higher resolution than SNP array-based methods.
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