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On the Role of Artificial Intelligence in Genomics to Enhance Precision Medicine

Authors Álvarez-Machancoses Ó, DeAndrés Galiana EJ, Cernea A, Fernández de la Viña J, Fernández-Martínez JL

Received 2 December 2019

Accepted for publication 17 February 2020

Published 19 March 2020 Volume 2020:13 Pages 105—119

DOI https://doi.org/10.2147/PGPM.S205082

Checked for plagiarism Yes

Review by Single-blind

Peer reviewer comments 2

Editor who approved publication: Dr Martin H. Bluth


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, Israel

Correspondence: Juan Luis Fernández-Martínez
Group of Inverse Problems, Optimization and Machine Learning, Department of Mathematics, University of Oviedo, C. Federico García Lorca, 18, Oviedo 33007, Spain
Email jlfm@uniovi.es

Abstract: 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


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