Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2022 Sep 9:15:914830.
doi: 10.3389/fnmol.2022.914830. eCollection 2022.

Precision machine learning to understand micro-RNA regulation in neurodegenerative diseases

Lucile Mégret  1 , Cloé Mendoza  1 , Maialen Arrieta Lobo  1 , Emmanuel Brouillet  1 , Thi-Thanh-Yen Nguyen  2 , Olivier Bouaziz  2 , Antoine Chambaz  2 , Christian Néri  1
Affiliations
Review

Precision machine learning to understand micro-RNA regulation in neurodegenerative diseases

Lucile Mégret et al. Front Mol Neurosci. .

Abstract

Micro-RNAs (miRNAs) are short (∼21 nt) non-coding RNAs that regulate gene expression through the degradation or translational repression of mRNAs. Accumulating evidence points to a role of miRNA regulation in the pathogenesis of a wide range of neurodegenerative (ND) diseases such as, for example, Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis and Huntington 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 be related to the lack of sufficiently rich or homogeneous data, such as 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 be related to the methodological challenges associated with the accurate system-level modeling of miRNA and mRNA data. 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.

Keywords: complex RNA-seq data; machine learning; miRNA regulation; neurodegenerative disease; precision analysis; shape analysis.

PubMed Disclaimer

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Simplified view of miRNA regulation of gene expression.
FIGURE 2
FIGURE 2
Examples of a mRNA expression surface negatively correlated with a miRNA expression surface in the striatum of HD mice.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

References

    1. Abiodun O. I., Jantan A., Omolara A. E., Dada K. V., Mohamed N. A., Arshad H. (2018). State-of-the-art in artificial neural network applications: A survey. Heliyon 4:e00938. 10.1016/j.heliyon.2018.e00938 - DOI - PMC - PubMed
    1. Altman N. S. (1992). An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression. Am. Statistician 46 175–185. 10.1080/00031305.1992.10475879 - DOI
    1. Bandyopadhyay S., Mitra R. (2009). TargetMiner: MicroRNA target prediction with systematic identification of tissue-specific negative examples. Bioinformatics 25 2625–2631. 10.1093/bioinformatics/btp503 - DOI - PubMed
    1. Berger J. O., Moreno E., Pericchi L. R., Bayarri M. J., Bernardo J. M., Cano J. A., et al. (1994). An overview of robust Bayesian analysis. Test 3 5–124. 10.1007/BF02562676 - DOI
    1. Betel D., Koppal A., Agius P., Sander C., Leslie C. (2010). Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites. Genome Biol. 11:R90. 10.1186/gb-2010-11-8-r90 - DOI - PMC - PubMed