CBNplot

CBNplot infers Bayesian networks from gene expression and functional enrichment analysis (EA) results to elucidate directed gene regulatory interactions within curated biological pathway databases.

Key Features:

• Bayesian Network Inference: CBNplot infers Bayesian networks (BNs) from gene expression data using EA outcomes to identify directed edges among genes within enriched pathways.
• Structure Learning and Visualization: It performs BN structure learning and generates visualizations of inferred network structures derived from EA results.
• Comparison with Reference Networks: It enables comparison of inferred networks against established reference networks to contextualize and validate inferred interactions.
• Integration of Gene-Related Information: It incorporates detailed gene-related information into network representations to reflect gene attributes within inferred networks.
• Probabilistic Reasoning: It computes probabilistic measures to assess likelihoods of alternative network configurations and relationships.

Scientific Applications:

• Cancer research (bladder cancer): Applied to analyze complex gene interactions in cancer studies, with demonstrations on bladder cancer-related datasets.
• Gene regulatory network elucidation: Used to elucidate molecular mechanisms and interactions within enriched pathways or gene sets.
• Cross-dataset transformation and validation: Employed to transform results across different datasets and validate analyses against established knowledge and literature.
• Pathway-focused analysis: Facilitates pathway-centric investigation by integrating EA results with curated biological pathway databases.

Methodology:

Integration of functional enrichment analysis (EA) results with Bayesian network structure learning and inference, incorporating curated biological pathway databases.