{
  "abstract": "Motivation\n\nReconstructing tissue-specific transcription factor (TF) networks remains challenging. TF-motif-based methods often lack functional validation, while expression-based methods struggle to distinguish direct binding from indirect regulation. Integration of diverse data types is necessary to accurately prioritize likely functional targets directly bound by TFs across human tissues.Results\n\nWe introduce METANet (Motif Expression TF Association Networks), a supervised ensemble learning framework that combines TF motifs, cis-regulatory element activity, and linear and nonlinear expression-derived features to predict TF binding. Applied to 36 human tissues, predicted METANet maps significantly outperform established methods in identifying direct, likely functional targets of TFs validated by ChIP-seq and gene ontology. Furthermore, METANet maps capture tissue-specific regulation comparable to existing methods, allowing the identification of reproducible gene-trait associations.Availability and implementation\n\nAll code and network maps are freely available at Zenodo https://doi.org/10.5281/zenodo.20614479.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Computer Science and Engineering, Washington University, St. Louis, MO 63130, United States.",
    "Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63110, United States.",
    "Division of Computational and Data Sciences, Washington University, St. Louis, MO 63130, United States.",
    "College of Information, University of Maryland, College Park, MD 20742, United States.",
    "Division of Statistical Genomics, Washington University School of Medicine, St. Louis, MO 63110, United States.",
    "Department of Genetics, Washington University School of Medicine, St. Louis, MO 63110, United States."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Wooseok J Jung",
      "orcid": "0000-0001-8439-2133"
    },
    {
      "affs": [
        1,
        2
      ],
      "name": "Sandeep Acharya",
      "orcid": "0000-0001-8046-0688"
    },
    {
      "affs": [
        3
      ],
      "name": "Daniel P Ruskin"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Shu Liao"
    },
    {
      "affs": [
        4
      ],
      "name": "Vaha Akbary Moghaddam"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Zolboo Erdenebaatar"
    },
    {
      "affs": [
        0,
        1,
        5
      ],
      "name": "Michael R Brent",
      "orcid": "0000-0002-8689-0299"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/fckf3zj97rr68.bib",
  "cited_by": 2,
  "doi": "10.1093/bioinformatics/btag738",
  "fulltext_url": "https://europepmc.org/articles/PMC13693595",
  "id": "fckf3zj97rr68",
  "issns": [
    "1367-4803",
    "1367-4811"
  ],
  "landing_url": "https://europepmc.org/article/MED/42841723",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1312|Genomics and Chromatin Dynamics"
    ]
  ],
  "openalex": "W7220834125",
  "pdf_url": "https://europepmc.org/articles/PMC13693595?pdf=render",
  "pmcid": "PMC13693595",
  "pmid": "42841723",
  "published": "2026-10-01",
  "publisher": "Oxford University Press (OUP)",
  "ris": "https://research.draconitic.com/paper/fckf3zj97rr68.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:10Z",
      "native_id": "MED:42841723",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42841723"
    },
    {
      "first_seen": "2026-10-30T12:19:38Z",
      "native_id": "10.1093/bioinformatics/btag738",
      "source": "crossref",
      "url": "https://academic.oup.com/bioinformatics/article/doi/10.1093/bioinformatics/btag738/8875709"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1312|Genomics and Chromatin Dynamics",
      "subfield": "molecular-biology"
    }
  ],
  "title": "METANet: a supervised ensemble learning framework for reconstructing direct and functional tissue-specific transcription factor networks.",
  "title_text": "METANet: a supervised ensemble learning framework for reconstructing direct and functional tissue-specific transcription factor networks.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:19:38Z",
  "url": "https://research.draconitic.com/paper/fckf3zj97rr68",
  "venue": "Bioinformatics (Oxford, England)",
  "versions": []
}