# saloniemi/lnp-optim architecture

> Architecture diagram of the GitHub repository saloniemi/lnp-optim, generated by GitDiagram from its file tree, README and a sample of source files (last updated 2026-11-12). It is an AI-made overview: check details against the source.

- Interactive diagram: https://gitdiagram.com/saloniemi/lnp-optim
- Repository: https://github.com/saloniemi/lnp-optim

## Overview

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`](https://github.com/saloniemi/lnp-optim/blob/main/Kumar.csv) (LNP dataset), [`Preprocessing.py`](https://github.com/saloniemi/lnp-optim/blob/main/Preprocessing.py) (Feature preparation), [`Baseline.py`](https://github.com/saloniemi/lnp-optim/blob/main/Baseline.py) (Baseline regressors), [`Evaluations.py`](https://github.com/saloniemi/lnp-optim/blob/main/Evaluations.py) (Metrics and plots)

## Diagram (Mermaid)

```mermaid
flowchart TD

subgraph group_data["Data preparation"]
  node_dataset["LNP dataset<br/>[Kumar.csv]"]
  node_preprocessing["Feature preparation<br/>[Preprocessing.py]"]
end

subgraph group_models["Baseline modeling"]
  node_baseline["Baseline regressors<br/>[Baseline.py]"]
end

subgraph group_evaluation["Evaluation and plots"]
  node_evaluations["Metrics and plots<br/>[Evaluations.py]"]
end

node_researcher(("Researcher"))

node_researcher -.->|"provides data"| node_dataset
node_dataset -.->|"loaded by"| node_preprocessing
node_preprocessing -->|"prepares features"| node_baseline
node_baseline -->|"evaluates models"| node_evaluations

click node_dataset "https://github.com/saloniemi/lnp-optim/blob/main/Kumar.csv"
click node_preprocessing "https://github.com/saloniemi/lnp-optim/blob/main/Preprocessing.py"
click node_baseline "https://github.com/saloniemi/lnp-optim/blob/main/Baseline.py"
click node_evaluations "https://github.com/saloniemi/lnp-optim/blob/main/Evaluations.py"

classDef toneNeutral fill:#f8fafc,stroke:#334155,stroke-width:1.5px,color:#0f172a
classDef toneBlue fill:#dbeafe,stroke:#2563eb,stroke-width:1.5px,color:#172554
classDef toneAmber fill:#fef3c7,stroke:#d97706,stroke-width:1.5px,color:#78350f
classDef toneMint fill:#dcfce7,stroke:#16a34a,stroke-width:1.5px,color:#14532d
classDef toneRose fill:#ffe4e6,stroke:#e11d48,stroke-width:1.5px,color:#881337
classDef toneIndigo fill:#e0e7ff,stroke:#4f46e5,stroke-width:1.5px,color:#312e81
classDef toneTeal fill:#ccfbf1,stroke:#0f766e,stroke-width:1.5px,color:#134e4a
class node_dataset,node_preprocessing toneBlue
class node_baseline toneAmber
class node_evaluations toneMint
class node_researcher toneIndigo
```

## Components

### Data preparation

- [LNP dataset](https://github.com/saloniemi/lnp-optim/blob/main/Kumar.csv): `Kumar.csv`
- [Feature preparation](https://github.com/saloniemi/lnp-optim/blob/main/Preprocessing.py): `Preprocessing.py`

### Baseline modeling

- [Baseline regressors](https://github.com/saloniemi/lnp-optim/blob/main/Baseline.py): `Baseline.py`

### Evaluation and plots

- [Metrics and plots](https://github.com/saloniemi/lnp-optim/blob/main/Evaluations.py): `Evaluations.py`

### Other components

- **Researcher**

## Connections

- Researcher → LNP dataset: provides data
- LNP dataset → Feature preparation: loaded by
- Feature preparation → Baseline regressors: prepares features (evidence: `Baseline.py`)
- Baseline regressors → Metrics and plots: evaluates models (evidence: `Baseline.py`)