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Article

Towards a Precision Medicine Approach Based on Machine Learning for Tailoring Medical Treatment in Alkaptonuria

1
Department of Biotechnology, Chemistry and Pharmacy, University of Siena, 53100 Siena, Italy
2
Toscana Life Sciences Foundation, 53100 Siena, Italy
3
Hopenly s.r.l., 41058 Vignola, Italy
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Alessandro Desideri
Int. J. Mol. Sci. 2021, 22(3), 1187; https://doi.org/10.3390/ijms22031187
Received: 20 November 2020 / Revised: 2 January 2021 / Accepted: 22 January 2021 / Published: 26 January 2021
(This article belongs to the Section Molecular Pharmacology)
ApreciseKUre is a multi-purpose digital platform facilitating data collection, integration and analysis for patients affected by Alkaptonuria (AKU), an ultra-rare autosomal recessive genetic disease. It includes genetic, biochemical, histopathological, clinical, therapeutic resources and quality of life scores that can be shared among registered researchers and clinicians in order to create a Precision Medicine Ecosystem (PME). The combination of machine learning application to analyse and re-interpret data available in the ApreciseKUre shows the potential direct benefits to achieve patient stratification and the consequent tailoring of care and treatments to a specific subgroup of patients. In this study, we have developed a tool able to investigate the most suitable treatment for AKU patients in accordance with their Quality of Life scores, which indicates changes in health status before/after the assumption of a specific class of drugs. This fact highlights the necessity of development of patient databases for rare diseases, like ApreciseKUre. We believe this is not limited to the study of AKU, but it represents a proof of principle study that could be applied to other rare diseases, allowing data management, analysis, and interpretation. View Full-Text
Keywords: alkaptonuria; rare disease; machine learning; precision medicine; data analysis; QoL scores alkaptonuria; rare disease; machine learning; precision medicine; data analysis; QoL scores
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MDPI and ACS Style

Spiga, O.; Cicaloni, V.; Visibelli, A.; Davoli, A.; Paparo, M.A.; Orlandini, M.; Vecchi, B.; Santucci, A. Towards a Precision Medicine Approach Based on Machine Learning for Tailoring Medical Treatment in Alkaptonuria. Int. J. Mol. Sci. 2021, 22, 1187. https://doi.org/10.3390/ijms22031187

AMA Style

Spiga O, Cicaloni V, Visibelli A, Davoli A, Paparo MA, Orlandini M, Vecchi B, Santucci A. Towards a Precision Medicine Approach Based on Machine Learning for Tailoring Medical Treatment in Alkaptonuria. International Journal of Molecular Sciences. 2021; 22(3):1187. https://doi.org/10.3390/ijms22031187

Chicago/Turabian Style

Spiga, Ottavia, Vittoria Cicaloni, Anna Visibelli, Alessandro Davoli, Maria Ausilia Paparo, Maurizio Orlandini, Barbara Vecchi, and Annalisa Santucci. 2021. "Towards a Precision Medicine Approach Based on Machine Learning for Tailoring Medical Treatment in Alkaptonuria" International Journal of Molecular Sciences 22, no. 3: 1187. https://doi.org/10.3390/ijms22031187

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