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We were able to assign functional and phenotype information to many of the missed genes using ComBlast, a tool that associates the query missed genes with existing data in COMBREX through sequence similarity methods.
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The labels of these nearest neighbours associate the query word with the corresponding protein families.
It can be seen that all these five associated genes have at least six evidences of biological associations, suggesting that they are associated with the query gene in terms of diverse biological associations.
It is not surprising that the performance of the ab initio results are somewhat lower than the corresponding leave-one-out cross-validation results, since the inclusion of genes that are already known to be associated with the query disease should facilitate the discovery of additional genes associated with the disease.
We call the concepts that are directly associated with the query the pivot concepts, and the concepts that are indirectly associated with the query through those pivot concepts the target concepts.
However, it might be argued that a disease may be associated with more than one domain and that, consequently, the inclusion of domains already known to be associated with the query disease in the calculation of the domain proximity profile may ease the identification of novel associations.
PolySearch employs a text ranking scheme to score the most relevant sentences and abstracts that associate both the query and match terms with each other.
In the simulation, we use global information about messages in the network to decide when each query session ends, i.e., no message associated with the query session exists in the network.
Unlike previous methods, our method can rank the possible phenotypes associated with the query protein and shows a more comprehensive view of the protein's biological effects.
Our tests with de novo motif discovery suggest that we recover 12/38 of the known motifs associated with the query TF, and we identify significant motifs for 36/38 (p<0.01) of these TFs (see Table S1).
Given a query disease phenotype, a gene is not associated with the query phenotype.
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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