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We considered a GO term to be significantly overrepresented if the observed sample has more genes in the given term than 5% of 500 simulated data sets, and less than 5% probability of occurring by chance in the binomial sampling test.
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It requires going through with the vocabulary and synonyms data for the given term in the hierarchy of semantic relationship.
The term-dependent thresholding simply re-scores the detection by considering the confidence scores of all the detections of the given term in the ATWV formulation.
This result is especially indicative because there is state level variation in the number of years in a given term.
Six New York City Council seats will most likely go to openly LGBT candidates, the most ever in a given term.
G: number of genes annotated by a given term in the reference set.
O: number of genes annotated by a given term in the test set.
For a given set of GO term s), it can also identify the set of miRNAs whose targets are enriched in the given GO term(s).
*Count: the number of genes involved in the given GO term; %: the percentage of involved genes in total genes; P-Value: the modified Fisher Exact P-Value.
N i is set of documents in the given subset where term i appears.
Here n represents the number of abstracts that contain the given bio-terms in the literature set.
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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