hypogenic

You can generate testable scientific hypotheses from structured observational datasets by providing domain context, extracting dataset-specific labels, and applying configuration-driven LLM prompting to produce candidate explanations.

Yes, you can integrate literature evidence with empirical data for hypothesis refinement by combining insights extracted from research papers with observational patterns using HypoRefine and Union-style approaches.

Hypothesis inference and evaluation over a hypothesis bank works by running inference using dataset-specific label extraction to quantify support, contradiction, or neutrality against observational evidence.

The best way to systematically test competing explanations against observational evidence is to apply a consistent evaluation pipeline that uses dataset-aligned label extraction and iterative refinement for reliable inference.

Yes, you need a config.yaml file to generate hypotheses from your dataset, as the process requires configuration-driven LLM prompting to run the hypogenic_generation method and request a specified number of hypotheses.

You should not use automated hypothesis generation for research discovery when you lack structured observational datasets or domain context, as reliable inference requires dataset-aligned label extraction and iterative refinement.