data fusion
Definition
Data fusion is the computational process of integrating multiple heterogeneous data sources to create a unified, comprehensive representation that provides greater insight than individual datasets alone. In life sciences, this involves combining diverse biological data types—such as genomics, proteomics, metabolomics, clinical records, and literature-derived knowledge—to build holistic models of biological systems. Data fusion addresses challenges like data incompleteness, noise, and conflicting information through statistical integration methods, machine learning approaches, and knowledge graphs. It enables researchers to uncover hidden relationships, validate findings across platforms, and generate more robust hypotheses by leveraging complementary information from multiple experimental modalities and data repositories.
Visualize data fusion in Nodes Bio
Nodes Bio enables data fusion visualization by integrating multiple biological networks into unified graph structures. Researchers can overlay protein-protein interactions with gene expression data, pathway annotations, and disease associations to identify convergent mechanisms. Multi-layered network views reveal how different data types interconnect, while edge weighting and node attributes represent confidence scores from diverse sources, facilitating comprehensive systems-level analysis.
Visualization Ideas:
- Multi-layer networks showing integrated omics data with color-coded node types for genes, proteins, and metabolites
- Weighted edges representing confidence scores from multiple data sources with thickness indicating integration strength
- Composite disease networks merging genetic associations, protein interactions, and pathway data into unified causal models
Example Use Case
A cancer research team investigating resistance mechanisms to targeted therapy combines RNA-seq data showing differentially expressed genes, phosphoproteomics revealing activated signaling pathways, drug response databases, and patient clinical outcomes. Through data fusion, they construct an integrated network identifying a previously overlooked kinase that's upregulated in resistant tumors and connected to multiple survival pathways. This convergent evidence from diverse data types prioritizes the kinase as a promising combination therapy target, validated across molecular and clinical dimensions.