# A Knowledge Graph Framework for Dementia Research Data
## 4 Results
### Use Case: Graph-Enabled Phenotype, Flow, and Protein Exploration from AT(N) Biomarker Profiles
#### 4.1.2 Experimental Results  
A key objective of the DDI study is to comprehend the evolution of subjects across different disease states within the biological reality, and the AT(N) classification system is a pivotal reference point. The developed design patterns facilitate connections at various levels, enabling the exploration of individual and group trajectories across visits and expediting the retrieval of relevant phenotypes using graph queries (Figure 6). Figure 5: Mean execution times over ten runs for variable-length traversal queries between 1 and 10 connections, navigating from subject nodes to key Biolink categories. Using the AT(N) entities defined in the extensions ontology, we queried the graph database to investigate the flow between the different biomarker profiles. This exploration helped unravel the transitions between them at the cohort level, aiding in data filtering for parallel research endeavors. Moreover, presented visually (Figure 7), the outcomes of these queries proved instrumental in quality control efforts by highlighting unlikely transitions from pathological to normal states. Such interventions are vital since AT(N) profiles derive  
Figure 6: A DDI subject subgraph that illustrates study visits and associated phenotypes, visualized with Neo4j Bloom and further edited for readability. (**a**) An overview of longitudinal visits. Subjects are connected to each visit via the “biolink:participates_in” predicate. The logical sequencing of visits is established through the “biolink:precedes” predicate, facilitating query traversal. Clinical entity nodes represent associated medical processes (medical history, cognitive screenings, lab assays, and more), serving as the source of observations and conclusions while also supplying context and metadata for encounters and experimental setups. These nodes link to phenotype and disease entities to depict the outcomes of the clinical/research processes. (**b**) A specific visit branch tracing the path from the individual subject to the evaluated phenotypes and diseases noted during a medical history recording. Additional data from clinical entities are omitted to maintain clarity and uphold subject privacy. from biomarker measurements, where unexpected transitions may result from issues or errors in the respective assays. As shown in Figure 7b, one of the valuable attributes of KGs that incorporate domain ontologies is richer semantic querying. Leveraging the hierarchical structure within phenotype and disease ontologies, we exploited semantic querying to gather phenotypes spanning different domains and visualized their prevalence across the AT(N) profiles. As depicted in Figure 8, we focused on phenotypes extracted from the \"Abnormality of higher mental function\" class within the HP ontology. Phenotypes related to memory, language, and executive function were referenced based on the rules established for the norming items in the cognitive screening section of the dataset descriptor. Figure 7: Graph-based analysis illustrating the transitional flow among AT(N) biomarker profiles within the DDI cohort over successive protocol visits. (**a**) A Sankey diagram depicting the transitions in biomarker profiles. (**b**) The Cypher query utilized to calculate transition counts based on the predefined AT(N) biomarker profiles in the ontology. To capture complex graph structures into low-dimensional vector space, we utilized the GRAPE library to create node embeddings using the node2 Vec algorithm [102] with Skip Gram [103] and applied them to evaluate various aspects of the AT(N) biomarkers. We conducted an interesting experiment to investigate if the embeddings of subject visits showed any patterns in the low-dimensional space or were influenced by specific AT(N) profiles. Using t-SNE [104] to reduce the embeddings to two dimensions, we observed a clear tendency for Tau pathology to group together in the embedding space, suggesting shared characteristics among the phenotypes assessed in those visits.