Hypogenic Elite

A scientific computing skill for automated scientific hypothesis generation using Hypogenic — the AI-powered framework that generates testable hypotheses from experimental data and literature using large language models with systematic evaluation and ranking.

When to Use This Skill

Choose Hypogenic Elite when:

• Generating data-driven hypotheses from experimental results
• Systematically exploring hypothesis space for a research question
• Ranking and evaluating competing hypotheses against evidence
• Building automated scientific discovery pipelines

Consider alternatives when:

• You need manual hypothesis formulation (use domain expertise)
• You need literature review without hypothesis generation (use PubMed)
• You need statistical hypothesis testing (use scipy.stats)
• You need causal inference from data (use DoWhy or CausalML)

Quick Start

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claude "Generate hypotheses to explain differential gene expression patterns"

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from hypogenic import HypothesisGenerator, Evidence

# Initialize hypothesis generator
generator = HypothesisGenerator(
    model="gpt-4",
    domain="molecular biology"
)

# Provide experimental evidence
evidence = Evidence(
    observations=[
        "Gene X is upregulated 5-fold in tumor vs normal tissue",
        "Gene X expression correlates with poor survival (HR=2.3, p<0.001)",
        "Gene X encodes a kinase with known role in cell proliferation",
        "Inhibiting Gene X in cell lines reduces colony formation by 60%"
    ],
    context="Investigating novel therapeutic targets in pancreatic cancer"
)

# Generate hypotheses
hypotheses = generator.generate(
    evidence=evidence,
    n_hypotheses=5,
    constraints=["Must be testable in vitro", "Should suggest a mechanism"]
)

for i, hyp in enumerate(hypotheses, 1):
    print(f"\nHypothesis {i}: {hyp.statement}")
    print(f"  Mechanism: {hyp.mechanism}")
    print(f"  Testable prediction: {hyp.prediction}")
    print(f"  Confidence: {hyp.confidence:.2f}")
    print(f"  Supporting evidence: {len(hyp.supporting)}")

Core Concepts

Hypogenic Workflow

Step	Purpose	Output
Evidence Collection	Gather experimental observations	Structured evidence set
Hypothesis Generation	AI-driven hypothesis creation	Ranked hypothesis list
Evaluation	Score against evidence and literature	Confidence scores
Refinement	Iterate based on new data	Updated hypotheses
Prediction	Generate testable predictions	Experimental designs

Hypothesis Evaluation

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from hypogenic import HypothesisEvaluator

evaluator = HypothesisEvaluator(model="gpt-4")

# Evaluate a specific hypothesis
evaluation = evaluator.evaluate(
    hypothesis="Gene X promotes tumor growth through PI3K/AKT pathway activation",
    evidence=evidence,
    criteria=[
        "consistency_with_evidence",
        "mechanistic_plausibility",
        "novelty",
        "testability",
        "parsimony"
    ]
)

print(f"Overall score: {evaluation.score:.2f}")
for criterion, score in evaluation.criteria_scores.items():
    print(f"  {criterion}: {score:.2f}")

Iterative Refinement

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# Add new experimental results
new_evidence = Evidence(
    observations=[
        "PI3K inhibitor LY294002 blocks Gene X-induced proliferation",
        "Gene X does NOT activate MAPK/ERK pathway",
        "Phosphoproteomics shows AKT substrates are upregulated"
    ]
)

# Refine hypotheses with new evidence
refined = generator.refine(
    hypotheses=hypotheses,
    new_evidence=new_evidence
)

for hyp in refined:
    print(f"{hyp.statement} (confidence: {hyp.confidence:.2f})")

Configuration

Parameter	Description	Default
model	LLM for hypothesis generation	gpt-4
domain	Scientific domain context	Required
n_hypotheses	Number of hypotheses to generate	5
temperature	LLM sampling temperature	0.7
evaluation_criteria	Scoring criteria	[plausibility, testability, novelty]

Best Practices

1. Provide specific, quantitative evidence. "Gene X is upregulated 5-fold (p < 0.001)" generates better hypotheses than "Gene X is increased." Quantitative detail constrains the hypothesis space and improves mechanistic reasoning.

2. Include both positive and negative results. Negative results (what didn't happen) are as informative as positive results for hypothesis generation. "Gene X does NOT activate MAPK" eliminates certain mechanistic pathways.

3. Set domain context explicitly. The same observations may lead to different hypotheses in cancer biology vs. immunology vs. neuroscience. Set the domain to guide hypothesis generation toward relevant mechanisms.

4. Validate hypotheses against existing literature. Generated hypotheses should be checked against published findings. Use PubMed searches to verify whether the hypothesis is truly novel or has already been tested.

5. Use iterative refinement as a discovery loop. Generate initial hypotheses, design experiments to test the most promising ones, collect new data, and feed results back into the generator. This creates a systematic discovery cycle.

Common Issues

Generated hypotheses are too vague or generic. Provide more specific evidence and tighter constraints. Instead of "should suggest a mechanism," specify "should identify a specific signaling pathway and predict a measurable downstream effect."

Confidence scores don't correlate with quality. AI-generated confidence scores are estimates, not statistical measures. Use them for relative ranking within a set, not as absolute quality indicators. Always apply domain expertise to evaluate generated hypotheses.

Hypotheses contradict known biology. The LLM may generate plausible-sounding but biologically incorrect hypotheses. Always have a domain expert review generated hypotheses before designing experiments. Use the evaluation step to flag inconsistencies with established knowledge.