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We developed a framework to extract more than 1400 features from standard mobile phone data and used them to predict useful individual characteristics and group estimates.
After selecting relevant descriptors for each ORN (cf. next section), we trained 30,000 ANN models per ORN, selected those with the highest predictive power, and used them to predict ORN responses to 21 compounds, which were subsequently tested in vivo (in the following referred to as "test data").
In order to assess the predictive power of the binding probabilities, we first used them to predict known TF target gene interactions from the Incyte YPD database by using a 5-fold cross-validation approach.
They took the customer's ratings from, say, 2001, and used them to predict their ratings for 2002.
We determined Forchheimer's and Darcy's in-situ coefficients, and used them to predict flow rate in a denitrification bed.
We used them to predict the occurrence of four bird species with narrow and complementary structural habitat requirements, together being indicative of structurally diverse forests.
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Once the different putative orthologs had been annotated as described above, we used them to predicting the different metabolic pathways exhaustively in the completely sequenced fungi under study.
We developed a framework to extract more than 1400 features from standard mobile phone data and to use them to predict useful individual characteristics and group estimates.
16 Our work extends this effort by introducing a new burden score, integrating it with other sociodemographic and clinical measures, using them to predict specific outcomes, and validating a prediction tool in more than 75 000 patients admitted across two hospital systems.
The utility of inferred relationship matrices can be validated in the normal manner that is, by using them to predict genetic merit for complex phenotypes using best linear unbiased prediction (BLUP) and evaluating their accuracy.
Chin et al. [16] propose an exaggeration mapping (EM) method that transforms the facial motions in exaggerated motions and use them to predict the Myers-Briggs Type Indicator (MBTI) personality traits with an overall prediction accuracy of 60%.
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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