On the Role of Artificial Intelligence in Genomics to Enhance Precision Medicine - Álvarez-Machancoses Ó; DeAndrés Galiana EJ; Cernea A; Fernández de la Viña J; Fernández-Martínez JL (Dove Medical Press, 2020)
Óscar Álvarez-Machancoses,1,2 Enrique J DeAndrés Galiana,1 Ana Cernea,1 J Fernández de la Viña,1 Juan Luis Fernández-Martínez2 1Group of Inverse Problems, Optimization and Machine Learning, Department of Mathematics, University of Oviedo, Oviedo 33007, Spain; 2DeepBiosInsights, NETGEV (Maof Tech), Dimona 8610902, IsraelCorrespondence: Juan Luis Fernández-MartínezGroup of Inverse Problems, Optimization and Machine Learning, Department of Mathematics, University of Oviedo, C. Federico García Lorca, 18, Oviedo 33007, SpainEmail jlfm@uniovi.esAbstract: The complexity of orphan diseases, which are those that do not have an effective treatment, together with the high dimensionality of the genetic data used for their analysis and the high degree of uncertainty in the understanding of the mechanisms and genetic pathways which are involved in their development, motivate the use of advanced techniques of artificial intelligence and in-depth knowledge of molecular biology, which is crucial in order to find plausible solutions in drug design, including drug repositioning. Particularly, we show that the use of robust deep sampling methodologies of the altered genetics serves to obtain meaningful results and dramatically decreases the cost of research and development in drug design, influencing very positively the use of precision medicine and the outcomes in patients. The target-centric approach and the use of strong prior hypotheses that are not matched against reality (disease genetic data) are undoubtedly the cause of the high number of drug design failures and attrition rates. Sampling and prediction under uncertain conditions cannot be avoided in the development of precision medicine.Keywords: artificial intelligence, big data, genomics, precision medicine, drug design