International Journal of Molecular Sciences Article Robust Sampling of Defective Pathways in Alzheimer’s Disease. Implications in Drug Repositioning Juan Luis Fernández-Martínez 1,2,* , Óscar Álvarez-Machancoses 1,2 , Enrique J. deAndrés-Galiana 1,3 , Guillermina Bea 1 and Andrzej Kloczkowski 4,5 1 Group of Inverse Problems, Optimization and Machine Learning, Department of Mathematics, University of Oviedo, C/Federico García Lorca, 18, 33007 Oviedo, Spain;
[email protected] (Ó.Á.-M.);
[email protected] (E.J.d.-G.);
[email protected] (G.B.) 2 DeepBioInsights, C/Federico García Lorca, 18, 33007 Oviedo, Spain 3 Department of Informatics and Computer Science, University of Oviedo, C/Federico García Lorca, 18, 33007 Oviedo, Spain 4 Battelle Center for Mathematical Medicine, Nationwide Children’s Hospital, Columbus, OH 43205, USA;
[email protected] 5 Department of Pediatrics, The Ohio State University, Columbus, OH 43205, USA * Correspondence:
[email protected] Received: 27 April 2020; Accepted: 13 May 2020; Published: 19 May 2020 Abstract: We present the analysis of the defective genetic pathways of the Late-Onset Alzheimer’s Disease (LOAD) compared to the Mild Cognitive Impairment (MCI) and Healthy Controls (HC) using different sampling methodologies. These algorithms sample the uncertainty space that is intrinsic to any kind of highly underdetermined phenotype prediction problem, by looking for the minimum-scale signatures (header genes) corresponding to different random holdouts. The biological pathways can be identified performing posterior analysis of these signatures established via cross-validation holdouts and plugging the set of most frequently sampled genes into different ontological platforms. That way, the effect of helper genes, whose presence might be due to the high degree of under determinacy of these experiments and data noise, is reduced.