ArticleFrontiers in molecular biosciences2022
Inferring microRNA regulation: A proteome perspective.
Article in Frontiers in molecular biosciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Article
- Maize miRNAs Might Regulate Human Genes Involved in Prostate Cancer: An In Silico Approach.Biotech (Basel (Switzerland)) · 2025Article
- Specific GPCRs Elicit Unique Extracellular Vesicle MiRNA Array Signatures: An Exploratory Study.bioRxiv : the preprint server for biology · 2025Article
- Protein Language Models Expose Viral Immune Mimicry.Viruses · 2025Article
- Inferring single-cell and spatial microRNA activity from transcriptomics data.Communications biology · 2025Article
- Empowering prediction of miRNA-mRNA interactions in species with limited training data through transfer learning.Heliyon · 2024Article
- What's next? Forecasting scientific research trends.Heliyon · 2024Article
- Identification and validation of prognostic signature genes of bladder cancer by integrating methylation and transcriptomic analysis.Scientific reports · 2024Article
- Automated Street Light Adjustment System on Campus with AI-Assisted Data Analytics.Sensors (Basel, Switzerland) · 2023Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Post-transcriptional regulation in multicellular organisms is mediated by microRNAs. However, the principles that determine if a gene is regulated by miRNAs are poorly understood. Previous works focused mostly on miRNA seed matches and other features of the 3'-UTR of transcripts. These common approaches rely on knowledge of the miRNA families, and computational approaches still yield poor, inconsistent results, with many false positives. In this work, we present a different paradigm for predicting miRNA-regulated genes based on the encoded proteins. In a novel, automated machine learning framework, we use sequence as well as diverse functional annotations to train models on multiple organisms using experimentally validated data. We present insights from tens of millions of features extracted and ranked from different modalities. We show high predictive performance per organism and in generalization across species. We provide a list of novel predictions including
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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.