{
  "abstract": "MicroRNAs (miRNAs) are key post-transcriptional regulators, yet standard bulk and single-cell RNA-seq do not capture them, leaving this regulatory layer invisible in most transcriptomic data. We present miDGD, a deep generative decoder that jointly models paired mRNA and miRNA profiles through a shared latent representation, enabling miRNA expression to be predicted from mRNA alone. Trained on tumors (TCGA), healthy tissues (GTEx), and human cell lines, miDGD recovers hundreds of miRNAs in held-out tumors (mean Spearman {rho} = 0.56), captures both tissue-specific and ubiquitous miRNAs, and preserves known miRNA--target repression and host-gene co-expression. Without label supervision, its latent space separates 32 cancer types (80% accuracy). Predictions remain stable at single-cell-like sparsity and transfer across datasets--from tumors to healthy tissues and from bulk to single cells--where miDGD outperforms existing supervised and activity-inference methods. miDGD thus unlocks miRNA regulation in the vast body of existing mRNA-only data, including single-cell datasets.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Molecular Medicine, Aarhus University Hospital, Denmark"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "name": "F. Zamani"
    },
    {
      "name": "A. M. Rasmussen"
    },
    {
      "name": "V. Schuster"
    },
    {
      "name": "M. H. Diekema"
    },
    {
      "name": "A. Krogh"
    },
    {
      "affs": [
        0
      ],
      "name": "J. S. Pedersen"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6s8bccnkhdq5e.bib",
  "cited_by": 1,
  "doi": "10.64898/2026.05.29.727918",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.05.29.727918v2.full",
  "id": "6s8bccnkhdq5e",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.05.29.727918v2",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioinformatics"
    ],
    [
      "openalex",
      "1306|MicroRNA in disease regulation"
    ]
  ],
  "openalex": "W7163222177",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.05.29.727918v2.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-05-29",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/6s8bccnkhdq5e.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:55Z",
      "native_id": "10.64898/2026.05.29.727918",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.05.29.727918v2"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "A deep generative decoder predicts microRNA expression from bulk and single-cell mRNA profiles",
  "title_text": "A deep generative decoder predicts microRNA expression from bulk and single-cell mRNA profiles",
  "type": "preprint",
  "updated_at": "2026-10-30T12:20:55Z",
  "url": "https://research.draconitic.com/paper/6s8bccnkhdq5e",
  "venue": "bioRxiv",
  "versions": []
}