NetGen: a novel network-based probabilistic generative model for gene set functional enrichment analysis.

Area of Science:

• Bioinformatics
• Computational Biology
• Systems Biology

Background:

• High-throughput experiments generate vast biological data, but interpretation remains challenging.
• Gene Ontology (GO) enrichment analysis is common but often yields redundant terms, hindering biological insight.
• Current methods struggle with the complexity and redundancy in functional enrichment analysis.

Purpose of the Study:

• To develop a novel network-based probabilistic generative model, NetGen, for improved functional enrichment analysis.
• To address the challenge of redundant GO terms in interpreting high-throughput experimental data.
• To enhance the biological interpretability of gene set enrichment results.

Main Methods:

• Proposed NetGen, a network-based probabilistic generative model for functional enrichment.
• Integrated protein-protein interaction (PPI) networks to aid in identifying significantly enriched GO terms.
• Evaluated NetGen using simulation studies and four real-world biological datasets.

Main Results:

• NetGen demonstrated superior performance compared to existing methods in simulation studies.
• Analysis of real datasets identified relevant GO terms not directly linked to gene lists, correlating with disease literature.
• The method effectively reduces redundancy and improves the interpretability of functional enrichment results.

Conclusions:

• NetGen provides more reasonable and interpretable results for functional enrichment analysis.
• This term combination-based approach complements existing methods.
• NetGen aids in exploring the pathogenesis of complex diseases by offering deeper biological insights.