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license: cc-by-4.0 |
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# Nanobody Thermal Stability Dataset |
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## Dataset Overview |
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This dataset helps predict how stable nanobody sequences are at different temperatures. Thermal stability is important for nanobody engineering and applications, affecting how well they work in different environments. |
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The dataset includes two types of stability measurements: |
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- Melting temperature (Tm): The temperature at which nanobodies start to unfold |
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- Sequence stability: Stability scores based on sequence properties |
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## Data Collection |
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This dataset comes from experimental measurements of various nanobody sequences. The data is collected from published scientific literature and laboratory measurements, then clustering based split. |
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## Dataset Structure |
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The dataset is split into training, validation, and test sets: |
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- `train.csv`, `val.csv`, `test.csv` |
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### File Format |
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Each CSV file contains these columns: |
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- `seq`: Nanobody amino acid sequence |
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- `label`: Thermal stability value (melting temperature or stability score) |
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## Uses and Limitations |
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### Uses |
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- Develop machine learning models to predict nanobody thermal stability |
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- Help design more stable nanobodies |
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- Provide reference data for nanobody research |
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### Limitations |
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- Limited dataset size may not represent all nanobody families |
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- Experimental conditions may affect measurements |
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- Models should account for data distribution characteristics |
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## Evaluation Metrics |
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Model performance is evaluated using: |
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- Spearman correlation |
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- R² |
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- Root Mean Squared Error (RMSE) |
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- Mean Absolute Error (MAE) |
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