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Precision machine learning to understand micro-RNA regulation in neurodegenerative diseases
Lucile Mégret1, Cloé Mendoza1, Maialen Arrieta Lobo1
1Sorbonne Université, Centre National de la Recherche Scientifique UMR 8256, Paris, France.
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.
Insights
Micro-RNAs (miRNAs) regulate gene expression and are implicated in neurodegenerative diseases (NDs). This review highlights machine learning and shape analysis for modeling complex miRNA and mRNA data in ND research.
Area of Science:
- Molecular Biology
- Genetics
- Computational Biology
Background:
- Micro-RNAs (miRNAs) are key regulators of gene expression.
- miRNA dysregulation is increasingly linked to neurodegenerative diseases (NDs) like Alzheimer's and Huntington's disease (HD).
- Existing systems-level studies face challenges due to data limitations and methodological complexities in modeling miRNA-mRNA interactions.
Purpose of the Study:
- To critically review machine learning methods for analyzing miRNA and mRNA expression data.
- To highlight the utility of shape-analysis concepts for modeling high-dimensional omics data.
- To discuss the potential of these methods for advancing neurodegenerative disease research.
Main Methods:
- Review of machine learning algorithms applied to gene expression data.
- Emphasis on shape-analysis concepts for precise modeling of complex biological data.
- Discussion of methods applicable to time-series and cell-type-specific omics data.
Main Results:
- Identified challenges in current systems-level studies of miRNA regulation in NDs.
- Proposed shape-analysis as a valuable approach for modeling high-dimensional miRNA and mRNA data.
- Elaborated on the potential of advanced computational methods for complex omics data analysis.
Conclusions:
- Accurate system-level modeling of miRNA and mRNA data is crucial for understanding ND pathogenesis.
- Shape-analysis offers a promising solution for the challenges in modeling high-dimensional omics data.
- Advanced computational approaches can significantly enhance our understanding of miRNA roles in neurodegeneration.
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