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For any given kingdom in K, we assume a distinct set of subcellular localization classes that are appropriate for that kingdom.
Letting Sk represent the set of all subcellular localization classes for kingdom k, we use the ngLOC method to generate a separate probability distribution over Sk, where P sclmk|di) denotes the probability that domain di is localized into location sclmk∈Sk.
We calculate a similarity measure between two domains di and dj in kingdom k as: <img src="http://journals.plos.org/plosone/article/asset?id=info?doi/10.1371/journal.pone.0005096.e014.PNG" class= inline-graphic"/> This measure was chosen as it allows similarity to be captured based on the probability over all localization classes considered, and not solely on the most likely prediction.
It predicts 9 localization classes for animals/fungi and 10 plant classes (adding in chloroplast).
Figure S7: Distribution comparison of chromosome III proteins and the complete yeast proteome across localization classes.
(D ) Karyogram showing the chromosomal position of genes for nuclear, anterior, and posterior RNA localization classes.
Similar(43)
For example, the ER-Cytoplasm binary classifier determined whether a cell harbored the phenotypic signatures of ER localization class (positive) or Cytoplasm localization class (negative).
For each ortholog dataset, the number of ortholog sets in each localization class is listed.
(randomized) indicates the values obtained with the localization class labels randomly shuffled 100 times.
This leaf corresponds to the predicted localization class (development set in Supplementary Table S1).
We therefore compared the gene-level variables of each localization class and revealed that subcellular mRNAs had significantly longer 3′UTR sequences and this was more pronounced for the posterior localization class.
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