---
license: mit
task_categories:
  - tabular-classification
  - tabular-regression
tags:
  - biology
  - gene-expression
  - physics
  - infection-detection
  - radiation
  - biodosimetry
  - bioinformatics
  - cross-platform
  - distributional-features
  - cbrn
  - biosurveillance
  - scaling-laws
  - zipf
  - entropy
  - gini-coefficient
  - dose-response
pretty_name: CBRN Physics Features
size_categories:
  - 1K<n<10K
language:
  - en
---

# CBRN Physics Features

**Pre-computed physics-informed distributional features for pathogen-agnostic biological threat detection in gene expression data.**

## Overview

This dataset contains per-sample and per-group features computed from the *shape* of gene expression distributions rather than the identity of individual genes. The four core features — **Gini coefficient**, **Shannon entropy**, **normalized entropy**, and **Zipf exponent** — are platform-agnostic: they require no gene annotation, no probe mapping, and no platform-specific normalization.

These features enable:
- **Cross-platform infection detection** (AUROC 0.720 across 5 microarray/RNA-seq platforms)
- **Radiation biodosimetry** from tissue gene expression (AUROC 0.793)
- **Dose-response modeling** with monotonic feature-dose relationships

## Dataset Structure

| File | Description | Rows | Key Columns |
|------|-------------|-----:|-------------|
| `infection/per_sample_features.csv` | Per-sample features across 10 GEO infection datasets | 1,954 | `gini`, `shannon_entropy`, `normalized_entropy`, `zipf_gamma`, `dataset`, `condition` |
| `infection/cross_dataset_transfer.csv` | Cross-dataset transfer AUROC (train on one platform, test on another) | 51 | `train_dataset`, `test_dataset`, `auroc`, `model` |
| `infection/validation_transfer.csv` | Development-to-validation transfer with bootstrap 95% CI | 12 | `train_dataset`, `test_dataset`, `auroc`, `ci_low`, `ci_high` |
| `radiation/per_sample_features.csv` | Per-sample features for brain, spleen, and skin tissue | 82 | `tissue`, `radiation`, `group`, `gini`, `shannon_entropy` |
| `radiation/group_features.csv` | Taylor *k* exponent with bootstrap CI per condition group | 12 | `tissue`, `group`, `taylor_k`, `ci_low`, `ci_high` |
| `dose_response/per_sample_features.csv` | Dose-response features across radiation doses (0-13 Gy) | 82 | `tissue`, `dose_gy`, `gini`, `shannon_entropy` |

**Total**: 2,193 rows across 6 CSV files.

## Features

| Feature | Formula | Interpretation | Range |
|---------|---------|---------------|-------|
| Gini coefficient | Lorenz curve area | Expression inequality (0 = uniform, 1 = one gene dominates) | [0, 1] |
| Shannon entropy | H = -sum(p log p) | Expression diversity (nats) | [0, log n] |
| Normalized entropy | H / log(n_expressed) | Scale-free diversity, comparable across platforms | [0, 1] |
| Zipf exponent (gamma) | OLS slope of log(expr) vs log(rank) | Power-law decay rate of ranked expression | [-3, 0] |
| Taylor *k* | OLS slope of log(Var) vs log(Mean) | Noise scaling exponent (requires replicates) | [1, 2] |

## Source Data

Features were computed from publicly available gene expression datasets:

- **Infection**: 10 GEO datasets (GSE161731, GSE157103, GSE63990, GSE60244, GSE72829, GSE236713, GSE100150, GSE111368, GSE40012, GSE73072) spanning RNA-seq, Affymetrix, Illumina, and Agilent platforms
- **Radiation**: 3 NASA GeneLab datasets (OSD-202 brain, OSD-211 spleen, OSD-237 skin)

## Usage

```python
import pandas as pd

# Load infection features
features = pd.read_csv("infection/per_sample_features.csv")

# Binary label: infected vs healthy
features["infected"] = (features["condition"] != "Healthy").astype(int)

# Train a simple classifier
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score

X = features[["gini", "shannon_entropy", "normalized_entropy", "zipf_gamma"]]
y = features["infected"]
scores = cross_val_score(LogisticRegression(), X, y, cv=5, scoring="roc_auc")
print(f"5-fold AUROC: {scores.mean():.3f} +/- {scores.std():.3f}")
```

## Code

Full analysis pipeline, vendored feature computation library, and figure generation scripts:

[github.com/jang1563/cbrn-physics-features](https://github.com/jang1563/cbrn-physics-features)

## Citation

```bibtex
@software{kim2026physics,
  author = {Kim, Jangwoo},
  title = {Physics-Informed Distributional Features for CBRN Threat Detection},
  year = {2026},
  url = {https://github.com/jang1563/cbrn-physics-features},
  license = {MIT}
}
```