Machine Learning techniques and Polygenic Risk Score application to prediction genetic diseases

• Mena Mamani, Nibeth ^[1]
  1. [1] Universidad de Salamanca

     Universidad de Salamanca

     Salamanca, España

     
• Localización: ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, ISSN-e 2255-2863, Vol. 9, Nº. 1, 2020, págs. 5-14
• Idioma: inglés
• Enlaces
  • Texto completo
• 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.