4 From simple networks to multi-source integration

 

This chapter covers

  • Building KGs of increasing complexity from structured data and integrating them
  • Knowledge graph exploration examples
  • Analysis and query techniques
  • LLM-assisted interpretation of knowledge graph analysis results

Our journey on knowledge acquisition continues in this chapter. Chapter 3 shows how to leverage an ontology to build a first simple knowledge graph that can help clinicians to identify diseases and heal the patient starting from their symptoms. That first example of knowledge acquisition showed, in a concrete way, which kind of applications can be “empowered” by knowledge graphs. This chapter move from the previous one and extend our understanding of how to build knowledge graphs of growing complexity and size and how to leverage them to build more powerful intelligent advisor systems.

4.1 Biomedical Knowledge graphs and applications

4.2 Multi-omic applications

4.3 Pharmaceutical applications

4.3.1 LLM-Assisted Interpretation of Pathway Analysis Results

4.4 Clinical applications

4.4.1 LLM-Guided Clinical Decision Support Analysis

4.5 Summary

4.6 Reference