How saloniemi/lnp-optim works
This is a small research codebase for modeling lipid nanoparticle transfection efficacy. Its principal workflow is to prepare efficacy and formulation-feature data, fit baseline regressors, and evaluate models with cross-validation and plots. The source confirms that baseline models call preprocessing and evaluation functions. Kumar.csv is present, but no sampled code shows it being loaded or identifies a script entry point, so that connection remains unverified.
Main entry points: Kumar.csv LNP dataset · Preprocessing.py Feature preparation · Baseline.py Baseline regressors · Evaluations.py Metrics and plots
Components 5
Data preparation
- LNP dataset
Kumar.csv - Feature preparation
Preprocessing.py
Baseline modeling
- Baseline regressors
Baseline.py
Evaluation and plots
- Metrics and plots
Evaluations.py
Other components
- Researcher
Connections 4
- Researcher → LNP dataset: provides data
- LNP dataset → Feature preparation: loaded by
- Feature preparation → Baseline regressors: prepares features
Baseline.py - Baseline regressors → Metrics and plots: evaluates models
Baseline.py