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Título
Machine Learning techniques and Polygenic Risk Score application to prediction genetic diseases
Autor(es)
Materia
Machine Learning
Polygenic Risk Score
Genomic Data
Risk Prediction
Machine Learning
Polygenic Risk Score
Genomic Data
Risk Prediction
Fecha de publicación
2020-01-27
Editor
Ediciones Universidad de Salamanca (España)
Citación
ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 9 (2020)
Resumen
For the last 10 years and after important discoveries such as genomic understanding of the human being, there has been a considerable increase in the interest on research risk prediction models associated with genetic originated diseases through two principal approaches: Polygenic Risk Score and Machine Learning techniques. The aim of this work is the narrative review of the literature on Machine Learning techniques applied to obtaining the polygenic risk score, highlighting the most relevant research and applications at present. The application of these techniques has provided many benefits in the prediction of diseases, it is evident that the challenges of the use and optimization of these two approaches are still being discussed and investigated in order to have a greater precision in the prediction of genetic diseases.
URI
ISSN
2255-2863
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