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

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.

We introduce METANet (Motif Expression TF Association Networks), a supervised ensemble learning framework that combines TF motifs, cis-regulatory element activity, and linear and non-linear 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.

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

Supplementary data are available at Bioinformatics online.