---
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, Akerele AT, Jaworski J, 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.