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Recently, genomic selection strategies based on molecular marker data have emerged as a promising approach [ 5].
In those former studies, the same line panel, gene sequences, and marker data have been used.
This approach is particularly helpful in studies where marker data have been pooled from several sources since the quality of marker genotyping is likely to vary due to differing genotyping technologies and/or technical expertise.
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Breeding values predicted from marker data had a better predictive ability than estimates of aPA from pedigree-based relationships.
The concept of conducting a causality analysis based on genetic marker data has been explored by several authors [ 13- 22].
Nevertheless, MCLUST applied directly to the raw marker data had in our study only a low power to identify population structure (data not shown).
Since 1980, detection of quantitative trait loci (QTL) combining phenotypic information with molecular marker data has received considerable attention (Bernardo 2008).
We compared the detection frequencies of QTLs in the following 3 scenarios: (1) in the total population fingerprinted with the original 90 k SNP marker data, (2) in the reference population fingerprinted with the original 90 k SNP array, and (3) in the total panel of 371 lines for which non-available 90 k SNP marker data had been imputed.
In the absence of molecular marker data, these have typically been based on morphology or ecogeography and have likely misrepresented the diversity contained within the collection.
From a clinical point of view, IL1RN, MAL and TGM3 were not clearly identified as potential HNSCC markers, while few data have been published concerning the implication of FN1, KRT, MMP1, PLAU and SPARC in this type of cancer.
Although originally CEPs were discriminated from mature CECs mainly by the presence or absence of CD133 progenitor markers, more recent data have cast serious doubts on this premise (Timmermans et al, 2008; Yoder and Ingram, 2009).
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