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

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; drug design; genomics; precision medicine.

Figure 1

Leading diseases where AI is considered. Despite the vast amount of AI literature in healthcare, the research mainly concentrates around a few disease types: cancer and neurodegenerative diseases. Reproduced from: Jiang et al. Artificial intelligence in healthcare: past, present and future. Stroke Vascular Neurol. 2017;2:e000101.

Figure 2

Main applications of AI in healthcare. Reprdoduced from: Jiang et al. Artificial intelligence in healthcare: past, present and future. Stroke Vascular Neurol. 2017;2:e000101.⁴

Figure 3

Algorithm workflow of (A) Fisher’s ratio sampler; (B) Holdout sampler; (C) Random sampler; (D) Bayesian network. Reproduced from: Cernea et al. Robust pathway sampling in phenotype prediction. Application to triple nagtive cancer. BMC Bioinformatics. In press.

Figure 4

Phenotype centered network provided by Bayesian networks in the case of survival.