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We previously developed an algorithm RFECS to discover the most informative modifications associated with the classification or prediction of mammalian enhancers.
Thus, we conclude that the minimal set of modifications required to predict genes within 1% accuracy of the set of all modifications is between 2 and 4, with H3K36me3 and H3K79me1 being the most informative modifications.
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We applied this to three of the most informative histone modifications as a measure of applicability to a broad set of modifications.
In a previous study, we developed a random forest-based method (RFECS) that could effectively identify enhancers genome-wide and determine the most informative set of modifications required for this task (Rajagopal et al. 2013).
For example, Won et al. coupled HMM with simulated annealing to search for the most informative combination of histone modification marks [11].
Also, given the diversity of histone modifications, it is likely that the most informative contexts to sample will vary across modifications.
Now the biomarker set is not made of genes, but of the most informative transcription factors or histone modification ChIP-seq experiments that overlap with the highly expressed genes in subsets of cell lines or patients (Supplementary Figures S2 and S3 online).
However, it has been argued that the cooperative action of multiple histone modifications, variants, and TFs is functionally most informative [ 9, 10].
On computing variable importance for each of the histone modifications with respect to the aforementioned classification, we found H3K36me3 followed by H3K79me1 to be the most informative and H3K36me3 alone could classify the boundaries within 3% of the accuracy achieved using all 24 modifications (AUCk36me3/AUCall ∼96%).
As illustrated in their description of the two phases of liver development, integrating physiological knowledge about the development of different tissues with genome-wide modification profiles will likely be necessary to identify the most informative contexts to assay and to fully characterize developmental enhancer usage.
As the most informative RMGs reflect common CNA's or regions of epigenetic silencing, we suggest that these genomic modifications act co-operatively and additively to produce cancers with higher proliferative capacity.
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