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Peer-reviewedCC BYBioinformatics (Oxford, England)

METANet: a supervised ensemble learning framework for reconstructing direct and functional tissue-specific transcription factor networks.

  • Wooseok J Jung1,2
  • Sandeep Acharya2,3
  • Daniel P Ruskin4
  • Shu Liao1,2
  • Vaha Akbary Moghaddam5
  • Zolboo Erdenebaatar1,2
  • Michael R Brent1,2,6
6 affiliations
  1. 1Department of Computer Science and Engineering, Washington University, St. Louis, MO 63130, United States.
  2. 2Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63110, United States.
  3. 3Division of Computational and Data Sciences, Washington University, St. Louis, MO 63130, United States.
  4. 4College of Information, University of Maryland, College Park, MD 20742, United States.
  5. 5Division of Statistical Genomics, Washington University School of Medicine, St. Louis, MO 63110, United States.
  6. 6Department of Genetics, Washington University School of Medicine, St. Louis, MO 63110, United States.
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Abstract

Motivation

Reconstructing 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

We 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

All code and network maps are freely available at Zenodo https://doi.org/10.5281/zenodo.20614479.

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Cite

BibTeXRIS
BibTeX
@article{jung2026metanet,
  title = {{METANet: a supervised ensemble learning framework for reconstructing direct and functional tissue-specific transcription factor networks.}},
  author = {Wooseok J Jung and Sandeep Acharya and Daniel P Ruskin and Shu Liao and Vaha Akbary Moghaddam and Zolboo Erdenebaatar and Michael R Brent},
  journal = {Bioinformatics (Oxford, England)},
  year = {2026},
  month = oct,
  doi = {10.1093/bioinformatics/btag738},
  url = {https://europepmc.org/article/MED/42841723},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - METANet: a supervised ensemble learning framework for reconstructing direct and functional tissue-specific transcription factor networks.
AU  - Wooseok J Jung
AU  - Sandeep Acharya
AU  - Daniel P Ruskin
AU  - Shu Liao
AU  - Vaha Akbary Moghaddam
AU  - Zolboo Erdenebaatar
AU  - Michael R Brent
PY  - 2026
DA  - 2026/10/01
JO  - Bioinformatics (Oxford, England)
DO  - 10.1093/bioinformatics/btag738
UR  - https://europepmc.org/article/MED/42841723
AB  - Motivation  Reconstructing 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  We 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  All code and network maps are freely available at Zenodo https://doi.org/10.5281/zenodo.20614479.
SN  - 1367-4803
SN  - 1367-4811
ER  -