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nanzhen102/FORUM-TB-models
FORUM-TB-models is a machine learning model from nanzhen102. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as cc-by-nc-4.0.
Trained Random Forest classifiers for M. tuberculosis drug resistance prediction from whole-genome sequencing (WGS) data.
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Updated Jun 6, 2026
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.joblib256 MB · 100%
From the Hugging Face model README
Trained Random Forest classifiers for M. tuberculosis drug resistance prediction from whole-genome sequencing (WGS) data.
Part of Project FORUM — an open-science interpretable ML pipeline for TB AMR prediction, developed in collaboration with Noah The Microbialist.
| File | Drug | Test AUC-ROC | CV AUC-ROC | Size |
|---|---|---|---|---|
| rf_RIFAMPICIN_v2.joblib | Rifampicin | 0.975 | 0.969 ± 0.004 | 61MB |
| rf_ISONIAZID_v2.joblib | Isoniazid | 0.948 | 0.946 ± 0.008 | 48MB |
| rf_ETHAMBUTOL_v2.joblib | Ethambutol | 0.894 | 0.900 ± 0.007 | 77MB |
| rf_PYRAZINAMIDE_v2.joblib | Pyrazinamide | 0.886 | 0.883 ± 0.007 | 58MB |
from huggingface_hub import hf_hub_download
import joblib
# Download and load a model
path = hf_hub_download(
repo_id="nanzhen102/FORUM-TB-models",
filename="rf_RIFAMPICIN_v2.joblib"
)
rf = joblib.load(path)
Models expect a feature vector of 2,693 AMR gene positions encoded as integers (0=REF, 1=A, 2=T, 3=C, 4=G).
Download the ML-ready dataset directly from Kaggle: https://www.kaggle.com/datasets/nanzhen/forum-tb?resource=download
Top SHAP features confirmed against known resistance mutations:
CC BY-NC 4.0 — free for non-commercial use with attribution.