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For example, each gene in a list of DEGs has its own significance value, functional annotation, etc.
For example, each gene in category 1 expresses one or more transcript solely in melanocytes; each gene in category 4 expresses in both cell types but has no cross-library overlap in transcript type; and so on.
Many different genetic mosaics can be made in Drosophila and, for example, each gene can be tested to see if it is needed in the sending, in the receiving cells or in both.
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For example, for each gene, the median expression value of the P20 mammary samples was subtracted from the expression value in each L1 mammary sample.
For example, for each gene, the expression values derived from the mammary L1 samples were compared to the expression values derived from mammary P20 samples.
For cross-platform (Example 2 and 5) and cross-species (Example 3) predictions, each gene's expression level was standardized using its sample-wise mean and sample standard deviation in each dataset to adjust range of gene expression level between training and test datasets.
Support for the I-model came from 11 examples in which each gene duplicate specifically retained one of the two ancestral MEHEs, that is, of splice isoform separation.
Representative examples of immunostaining for each gene product are shown in Fig. S2 of the ESM.
The recently developed framework (Yu et al., 2014) for multi-label learning formulates the problem as that of learning a low-rank linear model Z ∈ ℝ d × L, where each example (gene) is represented by d features and has up to L labels (diseases).
For example, many SNPs in each gene might be needed to capture the genetic variation of the gene, and different sets of SNPs are likely to be associated with different developmental stages, with different aspects of the disorder, with different sexes, etc.
For example, each pair of genes has only four expression states (00, 01, 10, 11), and we can collect statistics by counting how many times each of these four states is encountered in health and in disease.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com