Precision machine learning to understand micro-RNA regulation in neurodegenerative diseases - Mégret, Lucile; Mendoza, Cloé; Arrieta Lobo, Maialen; Brouillet, Emmanuel; Nguyen, Thi-Thanh-Yen; Bouaziz, Olivier; Chambaz, Antoine; Néri, Christian (Frontiers in Molecular Neuroscience, 2022)
Micro-RNAs (miRNAs) are short (~21nt) non-coding RNAs that regulate gene expression through the degradation or translational repression of mRNAs. There is accumulating though contrasted evidence for the implication of miRNA regulation in the pathogenesis of several neurodegenerative diseases (NDs) such as Alzheimer's disease, Parkinson's disease, Amyotrophic lateral sclerosis and Huntington's disease (HD). Several systems level studies aimed to explore the role of miRNA regulation in NDs, but these studies remain challenging. Part of the problem may relate to the lack of sufficiently rich or homogeneous data, e.g. time series or cell-type-specific data obtained in model systems or human biosamples, to account for context dependency. Part of the problem may also relate to the methodological challenges associated with accurately modeling miRNA and mRNA data on a systems level. Here, we critically review the main families of machine learning methods used to analyze expression data, highlighting the added value of using shape-analysis concepts as a solution for precisely modeling highly dimensional miRNA and mRNA data such as the ones obtained in the study of the HD process, and elaborating on the potential of these concepts and methods for modeling complex omics data.