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Here, we focus on the task of utilizing network-derived clusters to uncover functional modules and predict protein functions.
Clustering is perhaps the most common approach for global network analysis, and is frequently applied to uncover functional modules and protein complexes, and to infer protein function (Bader and Hogue, 2003; Hartwell et al., 1999; Pereira-Leal et al., 2004; Rives and Galitski, 2003; Spirin and Mirny, 2003).
These techniques are widely used to uncover functional brain architectures.
Importantly, by CRISPR/Cas9-mediated genomic editing, we uncover functional hierarchy of constituent enhancers within the SLC25A37 super-enhancer.
To uncover functional relations, we assessed phenotypes induced by compounds that target DNA damage to S-phase (MMS, HU), generate pyrimidine dimers (UV), disrupt hydrogen bonding (formamide) or that inhibit transcription (6-AU).
We suggest that classifying tetraspanins, based on their primary sequences in conjunction with their gene structure, could uncover functional differences and be useful in the task of elucidating their functional roles.
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In addition to characterizing the utility of network-derived clusters in uncovering functional modules and predicting protein functions, a major contribution of our work is a framework that can be used in the future for evaluating how well a new clustering approach performs for these tasks.
On the contrary, greedy algorithms are found to be relatively efficient while uncovering functional modules from large biological networks.
Previous analysis of Alp7 and Alp14 uncovered functional distinctions between these two proteins.
A study of chemoreceptor gene families uncovered functional genes in CB4856 that are defective in N2 (Stewart et al. 2005).
However, none of the above explicitly considers the inverse expression relationships for module prediction, which has been very effective in terms of uncovering functional miRNA-target relationships.
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