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These include the further annotation and validation of novel clusters, the analysis of genes from small clusters, the identification of novel domains, and the prediction and discovery of new functions.
More empirical alternatives include the use of re-sampling methods (to compare genes from small subsets of samples and those from the full dataset) [3], [19], and the use of spike-in data for which a set of genes are differentially expressed by design [12], [20].
Such nested retained duplicates are usually dosage-sensitive genes from small gene families (and therefore have more protein protein interactions) (Aury et al. 2006; Freeling 2009).
We conclude that patient-specific prognosis results cannot be determined by looking at only gene expression, especially with only a small subset extracted from large numbers of genes from small patient samples.
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An effective strategy for maximizing the diversity of these libraries relies on the assembly of large genes from smaller fragments of synthetic DNA.
This approach allows evaluation of genes from smaller sampled regions, within the range of LD decay, instead of requiring complete candidate gene sequencing.
Except for a small number of nuclear genes from small-scale individual studies [ 35], DNA information for L. minor is today limited to its chloroplast DNA [ 30].
However, this will inevitably lead to an under-representation of genes from smaller tissues.
The extra temporal information available from time series data allows the learning of causal relationships between variables (genes) from smaller datasets.
Hence, recurrence scores all genes in a genotype equally while focality scores the genes depending on the size of the lesions they belong to, so that genes from smaller lesions score higher.
With the development of genomic and proteomic technologies over the past two decades the simultaneous screening of thousands of genes and gene products from small samples of tissue or body fluid has become possible [ 26– 29].
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