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license: cc-by-4.0 |
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# Nanobody Polyreactivity Prediction Dataset |
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## Dataset Overview |
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This dataset helps predict whether nanobodies will show polyreactivity - the tendency to bind to multiple unrelated antigens. Polyreactivity is usually an unwanted feature in therapeutic applications, as it can lead to side effects and reduced effectiveness. |
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Accurately predicting nanobody polyreactivity is important for screening high-quality therapeutic candidates and understanding the molecular basis of antibody specificity. |
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## Data Collection |
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The dataset is based on nanobody polyreactivity data measured in laboratory experiments and we collect it from public literature. Nanobodies are classified as polyreactive or non-polyreactive based on their binding to these different antigens. |
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## Dataset Structure |
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The dataset is split into training, validation, and test sets. |
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### File Format |
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CSV files contain these columns: |
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- `seq`: Nanobody amino acid sequence |
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- `label`: Binary label indicating polyreactivity (1 for high polyreactivity, 0 for low polyreactivity) |
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## Uses and Limitations |
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### Uses |
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- Develop models to predict nanobody polyreactivity |
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- Screen for highly specific nanobody candidates |
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- Understand sequence features and molecular basis of polyreactivity |
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### Limitations |
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- Experimental methods for measuring polyreactivity may vary |
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- Polyreactivity exists on a spectrum rather than as a strict binary property |
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- Different experimental conditions may affect polyreactivity |
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## Evaluation Metrics |
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Model performance is evaluated using: |
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- Accuracy |
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- F1 Score |
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- Precision |
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- Recall |
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- AUROC (Area Under the Receiver Operating Characteristic curve) |
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- AUPRC (Area Under the Precision-Recall Curve) |
