ArticlePLoS computational biology2018
miRAW: A deep learning-based approach to predict microRNA targets by analyzing whole microRNA transcripts.
Article in PLoS computational biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers.
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Who cites it
56 citing papers in PubMed.
- MiRformer: a dual-transformer-encoder framework for predicting microRNA-mRNA interactions from paired sequences.Bioinformatics (Oxford, England) · 2026Article
- AI-Guided Patient-Derived Endothelial Modeling Identifies a Reversible Moyamoya-Specific miRNA Deficiency Corrected by Stem Cell-Extracellular Vesicles.Translational stroke research · 2026Article
- rbpCNN: a biophysics-informed deep learning model for predicting piRNA and mRNA interactions.Scientific reports · 2026Article
- Omic Profiling of Extracellular Vesicles from Two Cord-Related Sources Reveals Divergent Effects on Melanogenesis.Current issues in molecular biology · 2026Article
- MicroRNAs in oncology: a translational perspective in the era of AI.Nature reviews. Clinical oncology · 2026Review
- MicroRNA networks in rice seeds: unveiling key regulators of development, germination, and rice resilience under climate changes.Journal of experimental botany · 2026Review
- Progress and challenges in profiling protein-RNA and protein-associated RNA-RNA interactions.RNA (New York, N.Y.) · 2026Review
- miRNA regulation in brain tissue space: the 3'UTR perspective.RNA (New York, N.Y.) · 2026Review
- Retentive Network promotes efficient RNA language modeling of long sequences.Communications biology · 2026Article
- miRNAProtPred: computational prediction of human miRNA binding based on seed complementarity and thermodynamic stability.Frontiers in genetics · 2026Article
- Improved wound healing by dual inhibition of miR-146a-5p and miR-29a-3p supports a network action of dysregulated miRNAs in diabetic skin.Diabetologia · 2026Article
- Application of Artificial Intelligence Technology in Plant MicroRNA Research: Progress, Challenges, and Prospects.International journal of molecular sciences · 2025Review
- The power of microRNA regulation-insights into immunity and metabolism.FEBS letters · 2025Review
- miRBench: novel benchmark datasets for microRNA binding site prediction that mitigate against prevalent microRNA frequency class bias.Bioinformatics (Oxford, England) · 2025Article
- A Multi-Input Neural Network Model for Accurate MicroRNA Target Site Detection.Non-coding RNA · 2025Article
- Temporal Expression Analysis to Unravel Gene Regulatory Dynamics by microRNAs.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Regulatory QTLs affecting miRNA-mRNA interactions in cancer: mechanisms, methods, and clinical implications.Frontiers in molecular biosciences · 2025Review
- The Evolution of Nucleic Acid-Based Diagnosis Methods from the (pre-)CRISPR to CRISPR era and the Associated Machine/Deep Learning Approaches in Relevant RNA Design.Methods in molecular biology (Clifton, N.J.) · 2025Article
- AEmiGAP: AutoEncoder-Based miRNA-Gene Association Prediction Using Deep Learning Method.International journal of molecular sciences · 2024Article
- Gra-CRC-miRTar: The pre-trained nucleotide-to-graph neural networks to identify potential miRNA targets in colorectal cancer.Computational and structural biotechnology journal · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression by binding to partially complementary regions within the 3'UTR of their target genes. Computational methods play an important role in target prediction and assume that the miRNA "seed region" (nt 2 to 8) is required for functional targeting, but typically only identify ∼80% of known bindings. Recent studies have highlighted a role for the entire miRNA, suggesting that a more flexible methodology is needed. We present a novel approach for miRNA target prediction based on Deep Learning (DL) which, rather than incorporating any knowledge (such as seed regions), investigates the entire miRNA and 3'TR mRNA nucleotides to learn a uninhibited set of feature descriptors related to the targeting process. We collected more than 150,000 experimentally validated homo sapiens miRNA:gene targets and cross referenced them with different CLIP-Seq, CLASH and iPAR-CLIP datasets to obtain ∼20,000 validated miRNA:gene exact target sites. Using this data, we implemented and trained a deep neural network-composed of autoencoders and a feed-forward network-able to automatically learn features describing miRNA-mRNA interactions and assess functionality. Predictions were then refined using information such as site location or site accessibility energy. In a comparison using independent datasets, our DL approach consistently outperformed existing prediction methods, recognizing the seed region as a common feature in the targeting process, but also identifying the role of pairings outside this region. Thermodynamic analysis also suggests that site accessibility plays a role in targeting but that it cannot be used as a sole indicator for functionality. Data and source code available at: https://bitbucket.org/account/user/bipous/projects/MIRAW.
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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.