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README.md

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-license: cc-by-4.0
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+---
+license: cc-by-4.0
+---
+
+# Nanobody Polyreactivity Prediction Dataset
+
+## Dataset Overview
+
+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.
+
+Accurately predicting nanobody polyreactivity is important for screening high-quality therapeutic candidates and understanding the molecular basis of antibody specificity.
+
+## Data Collection
+
+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.
+
+## Dataset Structure
+
+The dataset is split into training, validation, and test sets.
+
+### File Format
+
+CSV files contain these columns:
+- `seq`: Nanobody amino acid sequence
+- `label`: Binary label indicating polyreactivity (1 for high polyreactivity, 0 for low polyreactivity)
+
+## Uses and Limitations
+
+### Uses
+- Develop models to predict nanobody polyreactivity
+- Screen for highly specific nanobody candidates
+- Understand sequence features and molecular basis of polyreactivity
+
+### Limitations
+- Experimental methods for measuring polyreactivity may vary
+- Polyreactivity exists on a spectrum rather than as a strict binary property
+- Different experimental conditions may affect polyreactivity
+
+## Evaluation Metrics
+
+Model performance is evaluated using:
+- Accuracy
+- F1 Score
+- Precision
+- Recall
+- AUROC (Area Under the Receiver Operating Characteristic curve)
+- AUPRC (Area Under the Precision-Recall Curve)