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Exploiting the interplay between cross-sectional and longitudinal data in Class III malocclusion patients

The aim of the study was to investigate how to improve the forecasting of craniofacial unbalance risk during growth among patients affected by Class III malocclusion. To this purpose we used computational methodologies such as Transductive Learning (TL), Boosting (B), and Feature Engineering (FE) in...

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Podrobná bibliografie
Vydáno v:Sci Rep
Hlavní autoři: Barelli, Enrico, Ottaviani, Ennio, Auconi, Pietro, Caldarelli, Guido, Giuntini, Veronica, McNamara, James A., Franchi, Lorenzo
Médium: Artigo
Jazyk:Inglês
Vydáno: Nature Publishing Group UK 2019
Témata:
On-line přístup:https://ncbi.nlm.nih.gov/pmc/articles/PMC6470156/
https://ncbi.nlm.nih.gov/pubmed/30996304
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1038/s41598-019-42384-7
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