Multimodal Disease Cohorts

Leveraging advanced data layers for dynamic network construction and temporal evolution in disease research.

Cohort Analysis

Constructing disease cohorts using multimodal longitudinal data for insights.

A dark themed graphic displaying a horizontal bar chart with different colored bars. The chart is titled 'Sintomas Registados' with categories labeled as Febre, Tosse, Dores Musculares, Cefaleias, and others. Numbers are aligned next to the bars indicating specific values for each symptom.

Dynamic Networks

Utilizing spatiotemporal graph neural networks to model biomarker interactions.

A medical setting with two people, likely healthcare professionals, focusing on a patient who is surrounded by numerous medical devices and monitors. The environment is busy with various tubes and equipment connected to the patient.

Knowledge Graph

Extracting gene-drug-phenotype associations to enhance biomedical research insights.

Reveal dynamic collaborative patterns of multimodal biomarkers in disease progression (e.g., the "metabolic reprogramming-immune dysregulation-organ damage" axis), providing new biomarker combination strategies for precision medicine.

Develop the first multimodal dynamic network model (MultiDynNet) enhanced by GPT-4 knowledge, supporting end-to-end clinical decision support (e.g., personalized nutrition intervention design).

Theoretical Breakthroughs:

Technological Tools:

Insights

Leveraging multimodal data for disease understanding.