Documentation
- Query Refinement
- Research Lenses
- AI-Powered Semantic Search
- Unbiased Computational Analysis
- Research Landscape
- Research Landscape Synthesis
- Trends and Momentum
- AI Research Assistant
- Citation-Grounded Answers
- Hypothesis Generation
- Methodology Generation
- Network Analysis
- Foundational Papers
- Hub Papers
- Bridge Papers
- Novel Leads
- PDF Export
Documentation
Hypothesis Generation
Generate empirically testable hypotheses directly from your evidence base. BioSkepsis uses AI to synthesize findings from your selected papers and propose novel hypotheses grounded in the literature, with every claim tied to specific source papers.
How It Works
Hypothesis generation follows a rigorous, evidence-based process:
- Evidence Analysis: The AI analyzes your selected papers, extracting key findings, mechanisms, and relationships
- Pattern Recognition: Identifies patterns, gaps, and potential connections across the literature
- Hypothesis Synthesis: Generates testable hypotheses based on mechanistic reasoning and identified patterns
- Citation Grounding: Every hypothesis is tied to specific PMIDs with either direct evidence or derived reasoning
Hypothesis Modes
Two modes are available depending on your research focus:
Fundamental Hypothesis
Discovery-driven hypotheses focused on basic biological mechanisms. Ideal for basic science research exploring molecular pathways and fundamental processes.
- Mechanism-first approach
- Focus on causal relationships
- Pathway-level predictions
Cross-Cutting Hypothesis
Integrative hypotheses that connect findings across different domains or research areas. Ideal for identifying unexpected connections and translational opportunities.
- Cross-domain synthesis
- Translational potential
- Novel connections
Hypothesis Structure
Each generated hypothesis includes:
- Core Statement: Clear, testable hypothesis statement
- Mechanistic Rationale: Explanation of the underlying biological reasoning
- Supporting Evidence: Key findings from source papers that support the hypothesis
- Predictions: Testable predictions that would confirm or refute the hypothesis
- Experimental Approach: Suggested approaches to test the hypothesis
- Citations: PMIDs with evidence type (Direct/Derived) for each claim
Best Practices
To get the best hypothesis generation results:
- Select relevant papers: Include papers that cover the key aspects of your research question
- Include diverse sources: Mix foundational and recent papers for comprehensive hypothesis generation
- Use the Research Landscape: Run landscape analysis first to identify hubs, bridges, and clusters that can inform hypothesis directions
- Verify citations: Always check cited papers to verify the evidence supports the hypothesis
- Iterate: Generate multiple hypotheses and refine based on your research priorities