feature engineering
Definition
Feature engineering is the process of transforming raw biological data into informative variables (features) that better represent underlying patterns for computational analysis and machine learning models. In life sciences, this involves creating meaningful representations from omics data, clinical measurements, or molecular properties—such as deriving protein interaction counts from network topology, calculating pathway enrichment scores, or extracting structural motifs from sequences. Effective feature engineering enhances model performance, reveals hidden biological relationships, and enables integration of heterogeneous data types. It requires domain expertise to identify which transformations capture relevant biological mechanisms while reducing noise and dimensionality in complex datasets.
Visualize feature engineering in Nodes Bio
Researchers can use Nodes Bio to engineer network-based features by visualizing and quantifying topological properties like node centrality, clustering coefficients, and shortest paths between disease genes. The platform enables extraction of subnetwork features, identification of hub proteins, and calculation of network proximity scores between drug targets and disease modules, transforming complex interaction data into actionable features for predictive modeling.
Visualization Ideas:
- Network topology maps highlighting engineered features like betweenness centrality and degree distribution across protein interaction networks
- Multi-layer networks showing how different feature types (expression, topology, sequence) integrate to predict phenotypes
- Comparative networks displaying before-and-after feature selection, emphasizing key nodes and edges that contribute most to predictive models
Example Use Case
A pharmaceutical team investigating Alzheimer's disease uses protein-protein interaction networks to engineer features predicting drug efficacy. They calculate network centrality measures for known disease proteins, identify bridging nodes between amyloid and tau pathways, and quantify the network distance between potential drug targets and disease modules. These engineered features—derived from network topology rather than expression data alone—improve their machine learning model's ability to prioritize candidate therapeutics by 40%, revealing that drugs targeting network hubs show greater clinical promise.