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Analysis of the false predictions has identified a number of areas for which insufficient information is available to improve the accuracy of the metabolic model in predicting growth phenotypes of single-gene knockout mutants.
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Similarly, nutrient utilization predictions have identified a number of compounds that lack clear pathways of entry into metabolism but are capable of supporting respiration and/or growth.
In this case, the gene predictions have identified a "best" filtered model (shown in yellow) as well as two further candidates.
Combination of deep sequencing approaches and bioinformatics predictions have identified 19,724 miRNAs related sequences across different phyla out of which 266 correspond to Arabidopsis miRNAs in miRBase v.17 [ 43]. miRNA regulation of nitrate-responsive genes has been shown to be a key mechanism of plant responses coordinating nitrate availability and root developmental responses.
In silico prediction has identified several potential miRNAs targeting EZH2 in colon cancer cells, and forced expression of hsa-miR-26a-5p and hsa-let7b-5p phenocopied the effects of EZH2 depletion in CRC cells, supporting a role of the two miRNAs in regulating EZH2 expression in colorectal cancer.
Co-expression relationship has been used to assign functional predictions to uncharacterized genes and has identified potential new members of many existing functional categories [ 4].
The MIT Technology Review has identified at least 18 different predictions about automation.
In fact, recent research has identified the need to incorporate vegetation dynamics processes7,8 and hydrodynamic effects9,10,11 to achieve more realistic predictions of wetlands response to sea-level rise.
Systematic reviews of studies developing prediction models have identified numerous models for predicting the same or similar outcome [ 1, 56- 59].
Risk prediction tools have identified a number of factors which impact on risk of death and myocardial infarction (MI) following an ACS event.
Several other prediction studies have identified early improvement in positive symptoms as the primary predictor of later response and non-response.
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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