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In no way am I predicting another raging bull market like the 1990s.
Specifically, we target two different tasks of paramount importance to increase the efficiency of energy producers and distributors and to meet consumers' peak demands: (i) predicting the daily average energy consumption and (ii) predicting the peak daily energy consumption.
The present QSAR model is helpful for (i) predicting self-assembled behaviors of peptides, (ii) scanning and identifying aggregation-forming sequences within proteins, (iii) understanding action mechanisms of peptide/protein aggregation, and (iv) designing potential self-assembled sequences applied as drug discovery and nano-materials.
Finding a score-weighted ensemble of solutions to this problem allows us to solve the related problems of (i) predicting ancestral interaction networks; (ii) imputing missing interactions in present-day networks; and (iii) inferring relative orders for duplication events that are consistent with a molecular clock.
Metabolic profiles and genome-scale metabolic networks (1) are used in various contexts, such as (i) predicting flux distribution for the metabolic activity over the network [metabolic control analysis (MCA) (2), flux balance analysis (FBA) (3) and constraint-based methods] and (ii) drug discovery and disease research (4 7).
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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