Identifying disease-causal genes using Semantic Web-based representation of integrated genomic and phenomic knowledge

Area of Science:

• Genomics and Bioinformatics
• Computational Biology
• Systems Biology

Background:

• Chronic diseases arise from complex interactions between genes, environmental factors, and etiologic events.
• Traditional genomic and transcriptomic studies identify numerous candidate genes, complicating causal inference.
• Prior knowledge integration is crucial for ranking and prioritizing potential disease-causing genes.

Purpose of the Study:

• To develop and apply an integrative genomics-phenomics approach for expedited disease candidate gene identification.
• To leverage Semantic Web technologies and network analysis for inferring likely causality roles of genes.
• To systematically utilize implicit relationships within large knowledge bases for hypothesis generation.

Main Methods:

• Construction of Semantic Web-based network data structures representing biological entities and their relationships.
• Application of centrality analyses on these networks to rank genes based on model-driven semantic relationships.
• Integration of genomic and phenomic data to identify functionally relevant candidate genes.

Main Results:

• Semantic Web approaches effectively leverage implicit relationships within extensive knowledge bases.
• Centrality analyses on network structures successfully identified key candidate genes.
• The proposed method facilitates the systematic identification of genes with potential causal roles in disease.

Conclusions:

• Integrative genomics-phenomics approaches combined with Semantic Web network analysis significantly enhance disease gene discovery.
• This methodology provides a powerful framework for prioritizing candidate genes and generating novel biological insights.
• Semantic Web technologies offer a scalable solution for complex biological data integration and causal inference.