Unanswered Questions

This section clarifies where uncertainty remains in your system or design. It helps delineate what parts of the project require deeper probing, model sensitivity checks, or exploratory analysis. Documenting these uncertainties early guides iteration and prevents premature conclusions.

Open or Speculative Aspects

• What key ideas or pathways remain poorly characterized?
  • Example: The mechanism linking methylation changes to phenotype remains indirect.
  • Example: The causal role of observed co-expression is unclear—correlation vs. regulation.

Assumptions Needing Validation

• What model or biological assumptions require stress-testing?
  • Example: Assuming batch-corrected RNA-seq preserves relative abundance.
  • Example: Assuming TF-gene linkages from public databases apply to your cell type.

Black Box Elements

• What components of your approach or system are opaque or hard to interpret?
  • Example: Feature importance in a deep learning model.
  • Example: Gene prioritization steps within a commercial tool.
  • Example: Pre-trained embeddings from a language model applied to genomic sequence.

Plans to Reduce Uncertainty

• What follow-up experiments or analyses could clarify these questions?
  • Example: Run perturbation experiments to test regulator-target relationships.
  • Example: Compare multiple algorithms to identify stable findings.