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Studying miRNA activity at the single-cell level presents a significant challenge due to the limitations of existing single-cell technologies in capturing miRNAs. To address this, we introduce two deep learning models: Cross-modality (CM) and single-modality (SM), both based on encoder-decoder architectures. These models predict miRNA expression at both bulk and single-cell levels using mRNA data. We evaluated the performance of CM and SM against the state-of-the-art miRSCAPE approach, using both bulk and single-cell datasets. Our results demonstrate that both CM and SM outperform miRSCAPE in accuracy. Furthermore, incorporating miRNA target information substantially enhanced performance compared to models that utilized all genes. These models provide powerful tools for predicting miRNA expression from single-cell mRNA data.
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A deep learning method for miRNA/isomiR target detection
Abstract Accurate identification of microRNA (miRNA) targets at base-pair resolution has been an open problem for over a decade. The recent discovery of miRNA isoforms (isomiRs) adds more complexity to this problem. Despite the existence of many methods, none considers isomiRs, and their performance is still suboptimal. We hypothesize that by taking the isomiR–mRNA interactions into account and applying a deep learning model to study miRNA–mRNA interaction features, we may improve the accuracy of miRNA target predictions. We developed a deep learning tool called DMISO to capture the intricate features of miRNA/isomiR–mRNA interactions. Based on tenfold cross-validation, DMISO showed high precision (95%) and recall (90%). Evaluated on three independent datasets, DMISO had superior performance to five tools, including three popular conventional tools and two recently developed deep learning-based tools. By applying two popular feature interpretation strategies, we demonstrated the importance of the miRNA regions other than their seeds and the potential contribution of the RNA-binding motifs within miRNAs/isomiRs and mRNAs to the miRNA/isomiR–mRNA interactions.
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- Award ID(s):
- 2120907
- PAR ID:
- 10368120
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Scientific Reports
- Volume:
- 12
- Issue:
- 1
- ISSN:
- 2045-2322
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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