Add canonical HetMoE checkpoints (ConvNet experts, hyperparams, ConvNet MoE, genomic zoo seeds 0/1/42) + model card
Browse filesThis view is limited to 50 files because it contains too many changes. See raw diff
- README.md +85 -0
- experts/ARID3A.pth +3 -0
- experts/FOXM1.pth +3 -0
- experts/GABPA.pth +3 -0
- experts/GATA3.pth +3 -0
- experts/JUND.pth +3 -0
- experts/MAX.pth +3 -0
- experts/SP1.pth +3 -0
- hyperparams/ARID3A.pth +3 -0
- hyperparams/FOXM1.pth +3 -0
- hyperparams/GABPA.pth +3 -0
- hyperparams/GATA3.pth +3 -0
- hyperparams/JUND.pth +3 -0
- hyperparams/MAX.pth +3 -0
- hyperparams/SP1.pth +3 -0
- moe/moe_model.pth +3 -0
- moe/moe_model_config.pth +3 -0
- zoo/seed0/DanQ_ARID3A.pth +3 -0
- zoo/seed0/DanQ_FOXM1.pth +3 -0
- zoo/seed0/DanQ_GABPA.pth +3 -0
- zoo/seed0/DanQ_GATA3.pth +3 -0
- zoo/seed0/DanQ_JUND.pth +3 -0
- zoo/seed0/DanQ_MAX.pth +3 -0
- zoo/seed0/DanQ_SP1.pth +3 -0
- zoo/seed0/DeepSEA_ARID3A.pth +3 -0
- zoo/seed0/DeepSEA_FOXM1.pth +3 -0
- zoo/seed0/DeepSEA_GABPA.pth +3 -0
- zoo/seed0/DeepSEA_GATA3.pth +3 -0
- zoo/seed0/DeepSEA_JUND.pth +3 -0
- zoo/seed0/DeepSEA_MAX.pth +3 -0
- zoo/seed0/DeepSEA_SP1.pth +3 -0
- zoo/seed1/DanQ_ARID3A.pth +3 -0
- zoo/seed1/DanQ_FOXM1.pth +3 -0
- zoo/seed1/DanQ_GABPA.pth +3 -0
- zoo/seed1/DanQ_GATA3.pth +3 -0
- zoo/seed1/DanQ_JUND.pth +3 -0
- zoo/seed1/DanQ_MAX.pth +3 -0
- zoo/seed1/DanQ_SP1.pth +3 -0
- zoo/seed1/DeepSEA_ARID3A.pth +3 -0
- zoo/seed1/DeepSEA_FOXM1.pth +3 -0
- zoo/seed1/DeepSEA_GABPA.pth +3 -0
- zoo/seed1/DeepSEA_GATA3.pth +3 -0
- zoo/seed1/DeepSEA_JUND.pth +3 -0
- zoo/seed1/DeepSEA_MAX.pth +3 -0
- zoo/seed1/DeepSEA_SP1.pth +3 -0
- zoo/seed42/DanQ_ARID3A.pth +3 -0
- zoo/seed42/DanQ_FOXM1.pth +3 -0
- zoo/seed42/DanQ_GABPA.pth +3 -0
- zoo/seed42/DanQ_GATA3.pth +3 -0
- zoo/seed42/DanQ_JUND.pth +3 -0
README.md
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---
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license: cc-by-nd-4.0
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tags:
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- TFBS
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- transcription-factor
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- genomics
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- mixture-of-experts
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- DNA
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library_name: pytorch
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pretty_name: ENCODE-TFBS Heterogeneous Mixture-of-Experts checkpoints
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datasets:
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- Lab-Rasool/ENCODE-TFBS
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---
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# ENCODE-TFBS — Heterogeneous Mixture-of-Experts checkpoints
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Trained model checkpoints for **robust transcription-factor binding-site (TFBS) prediction with a
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heterogeneous Mixture-of-Experts (MoE)**. A dense, soft MoE gates over per-expert *embeddings* from a
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heterogeneous expert zoo (modified-DeepBIND **ConvNet** + **DeepSEA** + **DanQ**, each probed to a
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common 32-dim embedding), which improves **out-of-distribution (OOD)** generalization to unseen
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transcription factors.
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These weights back the paper *"Robust Transcription Factor Binding Site Prediction and Explainability
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Using a Heterogeneous Mixture of Experts Architecture."* Code, training and evaluation pipeline:
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**https://github.com/lab-rasool/TFBS**. Training/eval data: **[Lab-Rasool/ENCODE-TFBS](https://huggingface.co/datasets/Lab-Rasool/ENCODE-TFBS)**.
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## Headline result (genomic, fair-negative protocol, 7 training factors)
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Feeding the unchanged embedding-gating MoE a heterogeneous expert pool beats a fine-tuned DNABERT-6
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baseline on the motif-bearing OOD strata, averaged over seeds 0/1/42:
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| Model | OOD AUC (mean ± std) |
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|---|---|
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| **HetMoE (this work)** | **0.821 ± 0.005** |
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| DNABERT-6 | 0.799 ± 0.008 |
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Margin **+0.022**. Per-seed: seed 42 → 0.827, seed 0 → 0.818, seed 1 → 0.819.
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## Repository contents
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```
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experts/<TF>.pth 7 ConvNet experts (modified DeepBIND), one per training TF
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hyperparams/<TF>.pth per-expert hyperparameters (reproduce training without Optuna)
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moe/moe_model.pth homogeneous ConvNet-only MoE gate (+ moe_model_config.pth)
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zoo/seed{0,1,42}/ heterogeneous zoo probes — DeepSEA_<TF>.pth, DanQ_<TF>.pth
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(E=32 FeatureProbeExpert heads over frozen DeepSEA/DanQ trunks)
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```
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The genomic HetMoE for a given seed is the **21-expert** pool: the 7 `experts/` ConvNets plus the
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14 `zoo/seed<N>/` DeepSEA + DanQ probes, with the `MixtureOfExperts` gate applied unchanged over the
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concatenated 32-dim embeddings. Only the three paper seeds (0, 1, 42) are published here.
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**Transcription factors.** Training: `ARID3A, FOXM1, GATA3, JUND, MAX, GABPA, SP1`. OOD evaluation is
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stratified into within-family, cross-family (e.g. CTCF, STAT3), cell-line-transfer, and a separately
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reported non-motif appendix — see `tfbs/constants.py` in the code repo.
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## Usage
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Install the `tfbs` package and load with the provided classes (`map_location` handles CPU-only nodes):
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```python
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from huggingface_hub import hf_hub_download
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import torch
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from tfbs.models import ConvNet, MixtureOfExperts # pip install -e . from github.com/lab-rasool/TFBS
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ckpt = hf_hub_download("Lab-Rasool/ENCODE-TFBS", "experts/GATA3.pth")
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state = torch.load(ckpt, map_location="cpu", weights_only=True)
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```
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See the GitHub repo's README and `experiments/hetmoe/` for the full caching + gating pipeline that
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rebuilds the heterogeneous MoE from these checkpoints. DNABERT-6 features are derived on the fly from
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`zhihan1996/DNA_bert_6`; no BERT weights are stored here.
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## Reproducibility
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The ConvNet conv bias (`wRect`) is a saved `nn.Parameter` and expert order is pinned to
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`tfbs.constants.TRAIN_TFS`, so re-running evaluation from these checkpoints is byte-identical on a
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given machine (minor device-numerics differences may remain across machines).
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## License
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`cc-by-nd-4.0`, matching the [ENCODE-TFBS dataset](https://huggingface.co/datasets/Lab-Rasool/ENCODE-TFBS).
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The underlying ENCODE data are from the ENCODE Project.
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</content>
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</invoke>
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experts/ARID3A.pth
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size 14015
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experts/FOXM1.pth
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experts/GABPA.pth
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experts/GATA3.pth
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experts/JUND.pth
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size 13987
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experts/MAX.pth
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experts/SP1.pth
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hyperparams/ARID3A.pth
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hyperparams/FOXM1.pth
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hyperparams/GABPA.pth
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hyperparams/GATA3.pth
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hyperparams/JUND.pth
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hyperparams/MAX.pth
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hyperparams/SP1.pth
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moe/moe_model.pth
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moe/moe_model_config.pth
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zoo/seed0/DanQ_ARID3A.pth
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