Business Context
BioTech Innovations, a leading biotechnology company, aims to optimize its protein purification processes to enhance yield and reduce costs. Accurate predictions of purification results are critical for scaling up production and improving product quality. The R&D team is exploring different modeling approaches to predict outcomes based on experimental features.
Dataset
| Feature Group | Count | Examples |
|---|
| Experimental Conditions | 10 | temperature, pH, ionic_strength, flow_rate |
| Protein Characteristics | 8 | molecular_weight, isoelectric_point, hydrophobicity, charge_density |
| Purification Results | 2 | yield_percentage, purity_level |
- Size: 5,000 experiments, 20 features
- Target: Continuous output — yield percentage and purity level
- Class balance: Not applicable (regression problem)
- Missing data: 5% missing in ionic_strength and flow_rate features
Requirements
- Develop a mechanistic model to predict protein purification outcomes based on known biochemical principles.
- Build a neural network model using the same dataset and compare its performance against the mechanistic model.
- Evaluate both models using RMSE and R² metrics.
- Provide insights on the trade-offs between interpretability and predictive power for both models.
Constraints
- The models should be interpretable enough to allow scientists to understand the predictions and underlying factors influencing purification results.
- The solution must be scalable to handle future experiments as more data becomes available.
