hypogeniclisted

# Hypogenic ## Overview Hypogenic provides automated hypothesis generation and testing using large language models to accelerate scientific discovery. The framework supports three approaches: HypoGeniC (data-driven hypothesis generation), HypoRefine (synergistic literature and data integration), and Union methods (mechanistic combination of literature and data-driven hypotheses). ## Quick Start Get started with Hypogenic in minutes: ```bash # Install the package uv pip install hypogenic # Clone example datasets git clone https://github.com/ChicagoHAI/HypoGeniC-datasets.git ./data # Run basic hypothesis generation hypogenic_generation --config ./data/your_task/config.yaml --method hypogenic --num_hypotheses 20 # Run inference on generated hypotheses hypogenic_inference --config ./data/your_task/config.yaml --hypotheses output/hypotheses.json ``` **Or use Python API:** ```python from hypogenic import BaseTask # Create task with your configuration task = BaseTask(config_path="./data/your_task/config.yaml") # Generate hypotheses task.generate_hypotheses(method="hypogenic", num_hypotheses=20) # Run inference results = task.inference(hypothesis_bank="./output/hypotheses.json") ``` ## When to Use This Skill Use this skill when working on: - Generating scientific hypotheses from observational datasets - Testing multiple competing hypotheses systematically - Combining literature insights with empirical patterns - Accelerating research discovery through automated hypothe