ExIR enables prioritizing driver and biomarker genes from omics data in a reference free manner

Area of Science:

• Genomics
• Systems Biology
• Bioinformatics

Background:

• High-throughput sequencing generates vast amounts of biological data.
• Identifying functionally relevant genes and proteins from this data is challenging.
• Existing methods often rely on external annotations, limiting their direct applicability.

Purpose of the Study:

• To present ExIR (experimental data-based integrative ranking), a novel data-driven framework.
• To classify and rank biological features (genes, proteins) as drivers, biomarkers, or mediators.
• To provide a method that operates directly on experimental data without external annotations.

Main Methods:

• ExIR infers association networks directly from experimental data.
• It classifies and ranks features based on their network behavior.
• The framework was validated across 14 transcriptomic and proteomic datasets.

Main Results:

• ExIR demonstrated consistently strong performance in feature prioritization compared to common methods.
• The framework successfully identified candidate regulators in a zebrafish mucopolysaccharidosis IIIA model.
• Prioritized features were associated with disease progression in the zebrafish model.

Conclusions:

• ExIR offers a generalizable approach for extracting biologically meaningful features from high-dimensional datasets.
• This method supports more efficient downstream experimental investigation.
• ExIR facilitates better interpretation of complex biological data for various research applications.