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| title: EPGFRE |
| emoji: 🧬 |
| colorFrom: green |
| colorTo: red |
| sdk: gradio |
| sdk_version: 6.18.0 |
| python_version: '3.13' |
| app_file: app.py |
| pinned: false |
| license: mit |
| short_description: E. coli Pan‑Genome Fluoroquinolone (FQ) Resistance Explorer. |
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| --- |
| title: E. coli Fluoroquinolone Resistance Explorer |
| emoji: |
| colorFrom: teal |
| colorTo: navy |
| sdk: gradio |
| sdk_version: 4.0.0 |
| app_file: app.py |
| pinned: false |
| license: mit |
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| --- |
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| # 🧬 E. coli Pan‑Genome Fluoroquinolone (FQ) Resistance Explorer |
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| **An open‑source machine learning pipeline for predicting fluoroquinolone resistance from pan‑genome presence/absence profiles.** |
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| [](https://huggingface.co/spaces/your-username/your-space-name) |
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| ## Overview |
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| A **random forest classifier** trained on binary presence/absence of **11,208** pan‑genome gene families from **2,715** *E. coli* genomes, with curated fluoroquinolone antibiograms from the Bacterial and Viral Bioinformatics Resource Center (BV‑BRC). |
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| The model achieves **AUC 0.914 ± 0.014** under five‑fold stratified cross‑validation, confirmed non‑random by a leakage‑corrected permutation test (**p = 0.001**, 1000 shuffles). |
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| --- |
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| ## Key Results |
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| | Metric | Value | |
| |--------|-------| |
| | **5‑fold CV AUC** | **0.914 ± 0.014** | |
| | **Permutation test p‑value** | **0.001** (1000 shuffles) | |
| | **Feature space** | **11,208** PGFam gene families | |
| | **Training genomes** | **2,715** *E. coli* | |
| | **Multi‑drug generalisation** | Median AUC **0.91** (37 drugs, range 0.634–0.996) | |
| | **Temporal holdout** | AUC **0.757** (≤2017 train, ≥2019 test) | |
| | **10‑gene clinical panel** | AUC **0.684** (Sens 0.726, Spec 0.601) | |
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| ### Key findings |
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| - The pan‑genome model outperforms a curated eight‑gene resistance‑marker baseline by **0.12 AUC points** (0.914 vs. 0.796). |
| - Top predictive features are **exclusively plasmid‑borne mobile genetic elements** (PemI/PemK toxin–antitoxin, IntI1 integrase, BlaTEM, BlaCTX‑M, Mph(A)) – **no direct fluoroquinolone resistance genes** (`gyrA`/`parC`/`qnr`) appear among the top predictors. |
| - The same feature matrix, retrained independently per antibiotic, generalises to **37 additional drug classes** (median AUC 0.91), supporting the **plasmid‑burden** interpretation. |
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| --- |
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| ## How to Use |
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| ### Predict tab (real model) |
| 1. **Upload a CSV** containing binary presence/absence (0/1) for the 11,208 gene families the model expects. |
| 2. **Click "Run prediction"** to get a resistance probability, classification, and visualisations. |
| 3. **Interpret the results** – the model returns: |
| - **Resistance probability** (0–100%) |
| - **Binary classification** (RESISTANT / SUSCEPTIBLE) at default 50% threshold |
| - **Feature coverage** – percentage of expected gene families found in your file |
| - **Driving genes** – bar chart of the most influential genes in your sample |
| - **Population context** – where your sample falls relative to the training cohort |
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| ### Explore tab (teaching sandbox) |
| - Toggle the 10 SHAP‑selected marker genes on/off to see how each one shifts the prediction. |
| - This panel alone reaches AUC **0.684** on held‑out genomes – well below the full model – and exists to build intuition, not to diagnose a real genome. |
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| --- |
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| ## File Requirements |
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| For the **Predict** tab to work, place these two files in the same directory as `app.py`: |
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| - **`model.joblib`** – the trained Random Forest model |
| - **`gene_features_list.pkl`** – the list of 11,208 PGFam feature names (in the exact order used during training) |
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| If these files are missing, the Predict tab will show a clear error message rather than silently falling back to a weaker model. |
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| --- |
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| ## Citation |
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| If you use this tool in your research, please cite the accompanying paper: |
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| > *[Your paper title].* [Journal/Conference], [Year]. DOI: [10.xxxx/xxxxx] |
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| --- |
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| ## License |
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| MIT © Zaeem Ahmad Mansoori |
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| --- |
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| ## Acknowledgements |
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| - **BV‑BRC** for genome annotations and antibiogram data |
| - **Zenodo** for the curated antibiogram dataset (record 15809334) |
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| --- |
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| ## Disclaimer |
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| This tool is a **research prototype**. It is not a clinical diagnostic, not a substitute for phenotypic susceptibility testing, and not validated outside the 2,715‑genome training cohort. Always confirm clinically relevant calls with laboratory methods. |
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| --- |
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| **Deployment instructions** (for your own Hugging Face Space): |
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| 1. Upload `app.py`, `requirements.txt`, `model.joblib`, and `gene_features_list.pkl` to your Space. |
| 2. Set SDK to **Gradio**. |
| 3. Commit and wait for the build to complete. |
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| --- |
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| *Built with ❤️ using Gradio, scikit‑learn, and BV‑BRC data.* |
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| Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference |
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