Models, data, and inferred networks for the paper "Conditioning a gene-regulatory foundation model on transcription-factor sequence enables ortholog-free cross-species transfer: the Nicotiana benthamiana network from soybean and Arabidopsis."

A masked-expression gene-regulatory foundation model (GRN-FM) with a single explicit TF×target weight tensor W is trained in a data-rich source species (soybean or Arabidopsis) and transferred zero-shot to data-poor N. benthamiana — either through reciprocal-best-hit orthologs, or, with no ortholog lookup for the regulators, by a sequence-conditioned model whose TF rows are generated from ESM-2 protein embeddings (W = φ(ESM(TF))·Vᵀ). Transfer is scored by relatedness-aware leave-study-out prediction of tobacco's own held-out-study expression (no regulatory gold standard is used).

Inferred TF→target edge lists (columns: TF, target, score): tobacco_within_clr.tsv, tobacco_within_grnboost2.tsv, tobacco_within_fm.tsv (within-species), and tobacco_transfer_from_arabidopsis.tsv (zero-shot Arabidopsis→tobacco transfer).

Code: https://github.com/k821209/nbgrn-transfer

python src/grn_transfer_eval.py     # ortholog transfer (soybean/Arabidopsis → tobacco)
python src/grn_transfer_seq.py      # sequence-conditioned, ortholog-free transfer
python src/grn_fewshot.py           # within-species data-efficiency curve

Within-species soybean GRN benchmark: dirmy/soygrn (DOI 10.57967/hf/9365).

Kang, Y. Conditioning a gene-regulatory foundation model on transcription-factor sequence enables ortholog-free cross-species transfer. (2026). Data & models: this repository.

License: CC-BY-4.0.