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The results show that even using low-quality ChIP-chip data, our method uncovers more relations than those inferred before from high-quality data.
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In addition to providing a new source of annotations, our method uncovered some annotation errors in Gemma.
In addition, our method uncovered a previously undetected modified histone site in the proximal promoter region of the CWF19-like one cell cycle control protein.
Second, our classification method uncovered a high degree of variation in the repeat consensus sequence both within and between species.
Furthermore, while we mainly focus on conserved topology-function relationships, our method also uncovers many species-specific ones.
We applied our method to uncover gene regulatory networks for the human cancer cell (Hela) cycle data.
We perform two leave-one-out cross-validation experiments to examine the capability of our method in uncovering genes that are known to be associated with certain diseases (i.e. disease genes) from a set of candidates.
The method uncovers interactions at a functional level and that do not require protein-protein contacts.
Overall, the method uncovers - in an unsupervised way - relevant biological topics and provides literature references for follow-up.
The results demonstrate that our method can effectively uncover the essential signaling components mediating a signal transduction process.
In essence, our method aims to uncover the dependencies between subcellular processes as well as the temporal ordering of the events associated with these processes.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com