ReviewFrontiers in molecular neuroscience2022
Precision machine learning to understand micro-RNA regulation in neurodegenerative diseases.
Review in Frontiers in molecular neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- MicroRNA bioinformatics in precision oncology: an integrated pipeline from NGS to AI-based target discovery.Journal of applied genetics · 2025Review
- DMGAT: predicting ncRNA-drug resistance associations based on diffusion map and heterogeneous graph attention network.Briefings in bioinformatics · 2025Article
- Advancing miRNA cancer research through artificial intelligence: from biomarker discovery to therapeutic targeting.Medical oncology (Northwood, London, England) · 2024Review
- Proteomics profiling and machine learning in nusinersen-treated patients with spinal muscular atrophy.Cellular and molecular life sciences : CMLS · 2024Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.