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README.md

@@ -2,7 +2,6 @@
 license: apache-2.0
 tags:
   - biology
-  - genomics
   - diffusion
   - single-cell
   - transcriptomics
@@ -29,6 +28,8 @@ model-index:
 # PhenoSeq: Image-Conditioned Diffusion for Single-Cell Transcriptomics
 
 [![HuggingFace](https://img.shields.io/badge/🤗%20HuggingFace-Sentinal4D/PhenoSeq-yellow)](https://huggingface.co/Sentinal4D/PhenoSeq)
+[![Paper](https://img.shields.io/badge/Paper-OpenReview-blue)](https://openreview.net/forum?id=ACHMa1e8J1)
+[![GitHub](https://img.shields.io/badge/GitHub-reednaidoo/PhenoSeq-black?logo=github)](https://github.com/reednaidoo/PhenoSeq)
 
 PhenoSeq is a Gaussian diffusion model that **generates scGPT RNA-seq embeddings conditioned on ViT-L microscopy imaging features**. Given fluorescence microscopy images of a cell or well, it predicts a 512-dimensional scGPT embedding representing the transcriptomic state of individual cells — enabling image-to-transcriptome translation at single-cell resolution.
 
@@ -131,20 +132,6 @@ The model was trained from scratch on scGeneScope using:
 python train.py --config config.yaml
 ```
 
-Key hyperparameters (see [config.yaml](config.yaml)):
-
-| Hyperparameter | Value |
-|---|---|
-| Batch size | 256 |
-| Learning rate | 1e-4 (cosine with warmup) |
-| Epochs | 5,000 (best at epoch 87) |
-| Model dim | 1,024 |
-| Attention heads | 8 |
-| Cross-attention layers | 6 |
-| Diffusion steps | 1,000 |
-| Schedule | Cosine |
-| EMA decay | 0.9999 |
-
 ## Data
 
 Training and evaluation data come from the [scGeneScope](https://huggingface.co/datasets/altoslabs/scGeneScope) dataset (Altos Labs):
@@ -202,7 +189,19 @@ Apache 2.0 — see [LICENSE](LICENSE).
 
 ## Citation
 
-If you use this model, please cite the scGeneScope dataset:
+If you use PhenoSeq, please cite our paper:
+
+```bibtex
+@inproceedings{naidoo2026phenoseq,
+  title     = {Cell Painting Generates Single-Cell Transcriptomics via Conditional Diffusion},
+  author    = {Naidoo, Reed and Hu, Jingyu and Tripodi, Giuseppe and Bakal, Chris and Chakraborti, Tapabrata},
+  booktitle = {ICML 2026 Workshop on Multi-modal Foundation Models and Large Language Models for Life Sciences (FM4LS)},
+  year      = {2026},
+  url       = {https://openreview.net/forum?id=ACHMa1e8J1}
+}
+```
+
+Please also cite the training dataset:
 
 ```bibtex
 @dataset{scgenescope,