This work aims to study the effects on mental health and brain structure of the time spent by children in different activities in front of a screen. Associations were found through the use of statistical models fitted on data from Adolescent Brain Cognitive Development longitudinal study, a US representative sample of almost 120000 children in age 8-12.
The idea of this work is to automatize the process of analysis and description of the SARS-CoV-2 virus starting from a sample of its genome and to be able to assign a group of samples to the correct variant. By using a clustering algorithm, in the end, it’s also possible to distinguish a new variant and obtain a description of its most common mutations.
Application of implicit neural representation with periodic activation function for Single Image Super-Resolution
Published in Proceedings of 14th International Conference on Bioinformatics and Computational Biology, 2022
The idea of this work is to automatize the process of analysis and description of the SARS-CoV-2 virus starting from a sample of its genome and to be able to assign a group of samples to the correct variant. By using a clustering algorithm, in the end, it’s also possible to distinguish a new variant and obtain a description of its most common mutations.
Recommended citation: Sofia Borgato, Marco Bottino, Marta Lovino, and Elisa Ficarra (2022). " SARS-CoV-2 variants classification and characterization " Proceedings of 14th International Conference on Bioinformatics and Computational Biology. https://doi.org/10.29007/5qpk
Published:
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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