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--- |
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tags: |
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- biology |
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- genomics |
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- classification |
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- sklearn |
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library_name: sklearn |
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--- |
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# BioGuard DNA Classifier Ensemble (Portable v1.1) |
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This repository contains a dual-model ensemble for DNA sequence analysis and virus classification, optimized for **portability and zero-dependency loading**. |
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> [!NOTE] |
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> **Version 1.1 Update**: This version has been refactored to decouple the models from custom feature extraction classes. It uses a raw scikit-learn format for maximum compatibility. |
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## 🧬 Models Included |
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This repository hosts two distinct models specialized for different aspects of genomic analysis: |
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### 1. **GenetiForest** (RandomForestClassifier) |
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* **File**: `dna_classifier.joblib` |
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* **Purpose**: General-purpose synthetic vs. biological sequence classification. |
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* **Architecture**: Random Forest (sklearn) with biological feature extraction (k-mers, GC content, etc.). |
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* **Performance (Test Set)**: |
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* **Accuracy**: 89.4% |
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* **F1 Score**: 89.4% |
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### 2. **ViralBoost** (GradientBoostingClassifier) |
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* **File**: `sequence_model.joblib` |
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* **Purpose**: Specific virus type identification (Influenza A, Norovirus, etc.) based on sequence signatures. |
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* **Architecture**: Gradient Boosting (sklearn) trained on real-world viral sequences. |
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* **Performance (Test Set)**: |
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* **Accuracy**: 99.4% |
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* **F1 Score**: 99.4% |
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* **Classes**: Other, Influenza A, Chicken anemia virus, Norovirus, Influenza B |
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## 🚀 Usage |
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Since these models use biological feature extraction, we provide a standalone `inference.py` script for easy usage. |
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1. Download all files (`.joblib` and `inference.py`). |
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2. Use the `inference.py` script: |
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```python |
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from inference import predict_dna |
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sequence = "ATGCTAGCTAGCTAG..." |
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results = predict_dna(sequence) |
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print(f"Genetic Type: {results['classification']}") |
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print(f"Virus Identity: {results['virus_identity']}") |
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``` |
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Alternatively, you can load components manually: |
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```python |
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import joblib |
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classifier = joblib.load("dna_classifier.joblib") |
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scaler = joblib.load("scaler_rf.joblib") |
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# (Refer to inference.py for Feature Extraction logic) |
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``` |
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## 📊 Training Meta |
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* **Generated By**: DNA Governance Console (vparka) |
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* **Framework**: scikit-learn |
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