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README.md

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+---
+language: en
+license: mit
+tags:
+  - drug-repurposing
+  - biomedical-nlp
+  - bert
+  - text-classification
+  - twas
+---
+
+# TRACE Classifier
+
+This is the fine-tuned BiomedBERT classifier for [TRACE](https://github.com/otienoco/TRACE) — TWAS-driven Repurposing through AI-assisted Curation of Evidence.
+
+## Model Description
+
+The classifier is built on [BiomedBERT](https://huggingface.co/microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext) and fine-tuned to classify PubMed abstracts for drug-gene relationships across three simultaneous tasks:
+
+- **Relationship detection** — does this abstract support a direct drug-gene interaction?
+- **Mechanism classification** — inhibitor, agonist, activator, antagonist, downregulator, upregulator, etc.
+- **Direction classification** — does the drug increase or decrease gene expression or activity?
+
+Held-out macro F1: **0.809** across all three tasks.
+
+## Intended Use
+
+This model is used as part of the TRACE pipeline. It is not intended to be used as a standalone model outside of that context. See the [TRACE GitHub repository](https://github.com/otienoco/TRACE) for full usage instructions.
+
+## Citation
+
+Otieno CO, Seagle HM, Guare L, Setia-Verma S, Velez Edwards DR, Edwards TL. TRACE: A fine-tuned biomedical language model for directionally informed drug repurposing from transcriptome-wide association studies. 2026. Preprint.