The Power of Knowledge Base Graphs in Biology for Disease
The integration of knowledge base graphs in biology, such as those involving protein-protein interactions (PPI), disease similarities, and regulatory networks, has significantly advanced our understanding and treatment of complex diseases. These knowledge graphs, formed from omics data interactions, including genomics, proteomics, metabolomics, and transcriptomics, provide a comprehensive view of molecular regulation and metabolism. This multi-layer approach has proven particularly useful in identifying disease mechanisms and improving drug responses.
One key application is in systems biology, where these graphs are used to model disease mechanisms and predict molecular interactions. This approach enhances therapeutic performance by enabling the repurposing of existing drugs and combining drug molecules to minimize off-target effects. Network structures within these graphs, encompassing various components like genes, proteins, and their interconnections, facilitate a systematic analysis. This network-based modeling leverages omics data for novel interaction prediction, studying disease comorbidity, and identifying distinct disease phenotypes.
Protein-protein interaction networks are particularly notable for their role in predicting disease-related proteins. These networks operate on the principle that shared components in disease-related PPI networks may lead to similar disease phenotypes. Additionally, drug-target interaction (DTI) networks have been employed for predicting drug responses, understanding new drug-target pair interactions, and analyzing the side effects of drug combinations.
Furthermore, these knowledge graphs are instrumental in predicting drug side effects and drug-drug interactions (DDI). By analyzing shared targets of different drugs, these networks can identify new therapeutic uses and combinations with enhanced efficacy.
The applications of knowledge base graphs in biology are vast and continually evolving. They play a crucial role in understanding complex biological systems, improving disease prediction, and enhancing drug development processes. This integrated approach, combining various layers of biological data, represents a significant step forward in personalized medicine and targeted therapeutics.
