Update README

README.md

@@ -28,7 +28,7 @@ license: mit
 
 ## Introduction
 
-Neurological diseases are the leading global cause of disability, yet most lack disease-modifying treatments. We present PROTON, a heterogeneous graph transformer that generates testable hypotheses across molecular, organoid, and clinical systems. To evaluate PROTON, we apply it to Parkinson's disease (PD), bipolar disorder (BD), and Alzheimer's disease (AD). In PD, PROTON linked genetic risk loci to genes essential for dopaminergic neuron survival and predicted pesticides toxic to patient-derived neurons, including the insecticide endosulfan, which ranked within the top 1.29\% of predictions. *In silico* PROTON screens reproduced six genome-wide $\alpha$-synuclein experiments, including a split-ubiquitin yeast two-hybrid system (normalized enrichment score [NES] = 2.30, FDR-adjusted $p < 1 \times 10^{-4}$), an ascorbate peroxidase proximity labeling assay (NES = 2.16, FDR $< 1 \times 10^{-4}$), and a high-depth targeted exome sequencing study in 496 synucleinopathy patients (NES = 2.13, FDR $< 1 \times 10^{-4}$). In BD, PROTON predicted calcitriol as a candidate drug that reversed proteomic alterations observed in cortical organoids derived from BD patients. In AD, we evaluated PROTON predictions in health records from $n$ = 610,524 patients at Mass General Brigham, confirming that five PROTON-predicted drugs were associated with reduced seven-year dementia risk (minimum hazard ratio = 0.63, 95% CI: 0.53–0.75, $p < 1 \times 10^{-7}$). PROTON generated neurological hypotheses that were evaluated across molecular, organoid, and clinical systems, defining a path for AI-driven discovery in neurological disease.
+Neurological diseases are the leading global cause of disability, yet most lack disease-modifying treatments. We present PROTON, a heterogeneous graph transformer that generates testable hypotheses across molecular, organoid, and clinical systems. To evaluate PROTON, we apply it to Parkinson's disease (PD), bipolar disorder (BD), and Alzheimer's disease (AD). In PD, PROTON linked genetic risk loci to genes essential for dopaminergic neuron survival and predicted pesticides toxic to patient-derived neurons, including the insecticide endosulfan, which ranked within the top 1.29% of predictions. *In silico* PROTON screens reproduced six genome-wide alpha-synuclein experiments, including a split-ubiquitin yeast two-hybrid system (normalized enrichment score [NES] = 2.30, FDR-adjusted *p* < 1e-4), an ascorbate peroxidase proximity labeling assay (NES = 2.16, FDR < 1e-4), and a high-depth targeted exome sequencing study in 496 synucleinopathy patients (NES = 2.13, FDR < 1e-4). In BD, PROTON predicted calcitriol as a candidate drug that reversed proteomic alterations observed in cortical organoids derived from BD patients. In AD, we evaluated PROTON predictions in health records from *n* = 610,524 patients at Mass General Brigham, confirming that five PROTON-predicted drugs were associated with reduced seven-year dementia risk (minimum hazard ratio = 0.63, 95% CI: 0.53–0.75, *p* < 1e-7). PROTON generated neurological hypotheses that were evaluated across molecular, organoid, and clinical systems, defining a path for AI-driven discovery in neurological disease.
 
 ![figure_1a](https://cdn-uploads.huggingface.co/production/uploads/643b2ce2c5f633a7fa82d507/0GXyvP495H6oS75juLlC-.png)
 
@@ -83,21 +83,17 @@ pretrain_model = pretrain_model.to(TORCH_DEVICE)
 ```
 
 
-## License
-
-PROTON is released under the [MIT License](https://github.com/mims-harvard/PROTON/blob/main/LICENSE).
-
-
 ## Citation
 
-If you use PROTON, please cite:
+PROTON is released under the [MIT License](https://github.com/mims-harvard/PROTON/blob/main/LICENSE). If you use PROTON, please consider citing our paper:
 ```
 @article{noori_graph_2025,
-  title={Graph AI generates neurological hypotheses validated in molecular, organoid, and clinical systems},
-  author={Noori, Ayush and Polonuer, Joaquin and Meyer, Katharina and Budnik, Bogdan and Morton, Shad and Wang, Xinyuan and Nazeem, Sumaiya and He, Yingnan and Arango, Iñaki and Vittor, Lucas and Woodworth, Matthew and Krolewski, Richard C. and Li, Michelle M. and Liu, Ninning and Kamath, Tushar and Macosko, Evan and Ritter, Dylan and Afroz, Jalwa and Henderson, Alexander B. H. and Studer, Lorenz and Rodriques, Samuel G. and White, Andrew and Dagan, Noa and Clifton, David A. and Church, George M. and Das, Sudeshna and Tam, Jenny M. and Khurana, Vikram and Zitnik, Marinka},
-  journal={arXiv preprint},
-  note={arXiv:XXXX.XXXXX (placeholder)},
-  year={2025}
+  title = {Graph {{AI}} generates neurological hypotheses validated in molecular, organoid, and clinical systems},
+  author = {Noori, Ayush and Polonuer, Joaqu{\'i}n and Meyer, Katharina and Budnik, Bogdan and Morton, Shad and Wang, Xinyuan and Nazeen, Sumaiya and He, Yingnan and Arango, I{\~n}aki and Vittor, Lucas and Woodworth, Matthew and Krolewski, Richard C. and Li, Michelle M. and Liu, Ninning and Kamath, Tushar and Macosko, Evan and Ritter, Dylan and Afroz, Jalwa and Henderson, Alexander B. H. and Studer, Lorenz and Rodriques, Samuel G. and White, Andrew and Dagan, Noa and Clifton, David A. and Church, George M. and Das, Sudeshna and Tam, Jenny M. and Khurana, Vikram and Zitnik, Marinka},
+  journal = {arXiv pre-print},
+  note = {arXiv:2512.13724},
+  year = 2025,
+  doi = {10.48550/arXiv.2512.13724},
 }
 ```