docs: add Hugo_symbols.tsv to file table + molecular tags

README.md

@@ -7,6 +7,8 @@ tags:
   - pathology
   - radiology
   - clinical
+  - molecular
+  - multi-omics
   - oncology
   - whole-slide-image
   - dicom
@@ -18,9 +20,9 @@ pretty_name: HoneyBee Sample Files
 
 # HoneyBee Sample Files
 
-Sample data for the [HoneyBee](https://github.com/Lab-Rasool/HoneyBee) framework — a scalable, modular toolkit for multimodal AI in oncology.
+Sample data and resource files for the [HoneyBee](https://github.com/Lab-Rasool/HoneyBee) framework — a scalable, modular toolkit for multimodal AI in oncology.
 
-These files are used by the HoneyBee example notebooks to demonstrate clinical, pathology, and radiology processing pipelines.
+These files are used by the HoneyBee example notebooks (clinical, pathology, radiology) and by HoneyBee's molecular processing code at runtime (`Hugo_symbols.tsv` is fetched on first use of DNA mutation preprocessing).
 
 **Paper**: [HoneyBee: A Scalable Modular Framework for Creating Multimodal Oncology Datasets with Foundational Embedding Models](https://arxiv.org/abs/2405.07460)
 **Package**: [`pip install honeybee-ml`](https://pypi.org/project/honeybee-ml/)
@@ -32,6 +34,7 @@ These files are used by the HoneyBee example notebooks to demonstrate clinical,
 | `sample.PDF` | Clinical | 70 KB | De-identified clinical report (PDF) for NLP extraction |
 | `sample.svs` | Pathology | 146 MB | Whole-slide image (Aperio SVS) for tissue detection, patch extraction, and embedding |
 | `CT/` | Radiology | 105 MB | CT scan with 2 DICOM series (205 slices total) for radiology preprocessing |
+| `Hugo_symbols.tsv` | Molecular | 128 KB | Hugo Gene Symbol vocabulary (17,312 symbols, one per line, no header) used by SeNMo's DNA mutation preprocessing. Ported from [lab-rasool/SeNMo](https://github.com/lab-rasool/SeNMo). Fetched automatically by `honeybee.processors.molecular.preprocessing.preprocess_dna_mutation()` on first use. |
 
 ### CT Directory Structure
 
@@ -47,4 +50,12 @@ CT/
 Tripathi, A., Waqas, A., Schabath, M.B. et al. HONeYBEE: enabling scalable multimodal AI in
 oncology through foundation model-driven embeddings. npj Digit. Med. 8, 622 (2025).
 https://doi.org/10.1038/s41746-025-02003-4
+```
+
+If your work uses `Hugo_symbols.tsv` (the molecular sample), also cite the SeNMo paper that originally curated this vocabulary:
+
+```bibtex
+Waqas, A., Tripathi, A., Ahmed, S. et al. Self-Normalizing Multi-Omics Neural Network for
+Pan-Cancer Prognostication. Int. J. Mol. Sci. 26, 7358 (2025).
+https://doi.org/10.3390/ijms26157358
 ```