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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

Nada Lukkahatai,1 Brian Walitt,2 Enrique J Deandrés-Galiana,3 Juan Luis Fernández-Martínez,3 Leorey N Saligan2 1Acute and Chronic Care, Johns Hopkins School of Nursing, Baltimore, MD, USA; 2Symptoms Biology Unit, National Institute of Nursing Research (NINR), Bethesda, MD, USA; 3Group of Inverse Problems, Optimization and Machine Learning, Applied Mathematics, University of Oviedo, Oviedo, Spain Objectives: Fibromyalgia syndrome (FMS) is a chronic and often debilitating condition that is characterized by persistent fatigue, pain, bowel abnormalities, and sleep disturbances. Currently, there are no definitive prognostic or diagnostic biomarkers for FMS. This study attempted to utilize a novel predictive algorithm to identify a group of genes whose differential expression discriminated individuals with FMS diagnosis from healthy controls. Methods: Secondary analysis of gene expression data from 28 women with FMS and 19 age- and race-matched healthy women. Expression of discriminatory genes were identified using fold-change differential and Fisher’s ratio (FR). Discriminatory accuracy of the differential expression of these genes was determined using leave-one-out-cross-validation. Functional networks of the discriminating genes were described from the Ingenuity’s Knowledge Base. Results: The small-scale signature contained 57 genes whose expressions were highly discriminatory of the FMS diagnosis. The combination of these high discriminatory genes with FR higher than 1.45 provided a leave-one-out-cross-validation accuracy for the FMS diagnosis of 85.11%. The discriminatory genes were associated with 3 canonical pathways: hepatic stellate cell activation, oxidative phosphorylation, and airway pathology related to COPD. Conclusion: The discriminating genes, especially the 2 with the highest accuracy, are associated with mitochondrial function or oxidative phosphorylation and glutamate signaling. Further validation of the clinical utility of this finding is warranted. Keywords: chronic pain, machine learning, medically unexplained symptoms, microarray