uncertainty quantification
Definition
Uncertainty quantification (UQ) is the systematic process of identifying, characterizing, and reducing uncertainties in computational models and experimental data analysis. In biological research, UQ addresses variability from measurement noise, biological heterogeneity, incomplete knowledge of molecular mechanisms, and model parameter estimation errors. It involves statistical methods to propagate uncertainties through analytical pipelines, assign confidence intervals to predictions, and distinguish between aleatory uncertainty (inherent randomness) and epistemic uncertainty (lack of knowledge). UQ is critical for assessing the reliability of computational predictions in systems biology, ensuring reproducible results, and making informed decisions about therapeutic targets or biomarker validation.
Visualize uncertainty quantification in Nodes Bio
Researchers can visualize uncertainty in network models by encoding confidence scores as edge weights or node attributes. Network edges representing protein-protein interactions or gene regulatory relationships can display probability ranges or bootstrap support values. Nodes Bio enables comparison of multiple network inference results, highlighting robust connections versus uncertain predictions, and allows filtering networks by confidence thresholds to focus on high-certainty biological relationships.
Visualization Ideas:
- Edge thickness proportional to confidence scores in protein interaction networks
- Color gradients representing uncertainty levels in gene regulatory networks
- Comparative networks showing consensus interactions across multiple inference methods with uncertainty bounds
Example Use Case
A systems biology team reconstructs a gene regulatory network from single-cell RNA-seq data to identify master regulators in cancer progression. Due to technical noise and dropout events in sequencing, each predicted regulatory edge has associated uncertainty. They perform bootstrap resampling and Bayesian network inference to quantify confidence in each interaction. By visualizing edges with uncertainty scores in Nodes Bio, they identify 15 high-confidence regulatory connections for experimental validation while flagging 40 uncertain edges requiring additional data collection.