MMiKG: a knowledge graph-based platform for path mining of microbiota-mental diseases interactions

Area of Science:

• Neuroscience
• Microbiology
• Computational Biology

Background:

• The microbiota-gut-brain axis describes bidirectional communication between the gut microbiota and the central nervous system.
• Existing knowledge on this axis is fragmented, hindering comprehensive understanding and AI development.
• This axis influences host mood, cognition, and behavior through complex interactions.

Purpose of the Study:

• To consolidate fragmented knowledge on the microbiota-gut-brain axis.
• To develop a structured knowledge graph (MMiKG) for visualizing and analyzing these interactions.
• To facilitate a deeper understanding of mental disorder pathogenesis and identify new therapeutic strategies.

Main Methods:

• Extensive literature review to collate information on the microbiota-gut-brain axis.
• Development of the Microbiota-Gut-Brain Axis Knowledge Graph (MMiKG).
• Utilizing GraphXR and Neo4j for visualization and data management.

Main Results:

• Creation of MMiKG, a comprehensive knowledge graph integrating diverse data resources.
• MMiKG enables visualization of connections between gut microbiota and the central nervous system.
• The platform facilitates exploration of potential links relevant to mental health.

Conclusions:

• MMiKG provides a unified platform for understanding the microbiota-gut-brain axis.
• The knowledge graph aids in comprehending mental disorder mechanisms.
• MMiKG offers novel insights for advancing therapeutic interventions and research in the field.