regression
Definition
Regression in biological systems refers to the reversal or reduction of a pathological state, disease progression, or developmental process back toward a normal or earlier condition. In causal frameworks, regression analysis statistically models relationships between dependent variables (outcomes like disease severity) and independent variables (factors like gene expression, drug dosage, or environmental exposures) to identify predictive associations and potential causal mechanisms. This approach quantifies how changes in upstream factors influence downstream biological outcomes, enabling researchers to map cause-effect relationships in complex biological networks and identify therapeutic targets that can drive disease regression.
Visualize regression in Nodes Bio
Researchers can visualize regression relationships as directed networks where nodes represent biological variables (genes, proteins, metabolites, clinical markers) and edges indicate regression coefficients or statistical significance. Network layouts can highlight which upstream factors most strongly predict downstream outcomes, revealing causal pathways that drive disease progression or regression. Multi-layer networks can integrate multiple regression models to show hierarchical causal relationships across molecular, cellular, and phenotypic levels.
Visualization Ideas:
- Weighted directed network showing regression coefficients between clinical variables and disease outcomes
- Multi-layer causal network linking genomic features to protein expression to phenotypic regression
- Time-series network displaying temporal regression relationships during disease progression or treatment response
Example Use Case
A cancer research team investigating tumor regression following immunotherapy treatment performs regression analysis on multi-omic data from patient biopsies. They model how baseline immune cell populations, cytokine levels, and tumor mutation burden predict treatment response. By mapping significant regression relationships as a network, they identify that high CD8+ T-cell infiltration combined with PD-L1 expression strongly predicts complete tumor regression, while specific metabolic pathway alterations correlate with resistance. This network reveals actionable biomarkers and combination therapy targets.