{"ID":2875985,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.01565","arxiv_id":"2509.01565","title":"Enabling Down Syndrome Research through a Knowledge Graph-Driven Analytical Framework","abstract":"Trisomy 21 results in Down syndrome, a multifaceted genetic disorder with diverse clinical phenotypes, including heart defects, immune dysfunction, neurodevelopmental differences, and early-onset dementia risk. Heterogeneity and fragmented data across studies challenge comprehensive research and translational discovery. The NIH INCLUDE (INvestigation of Co-occurring conditions across the Lifespan to Understand Down syndromE) initiative has assembled harmonized participant-level datasets, yet realizing their potential requires integrative analytical frameworks. We developed a knowledge graph-driven platform transforming nine INCLUDE studies, comprising 7,148 participants, 456 conditions, 501 phenotypes, and over 37,000 biospecimens, into a unified semantic infrastructure. Cross-resource enrichment with Monarch Initiative data expands coverage to 4,281 genes and 7,077 variants. The resulting knowledge graph contains over 1.6 million semantic associations, enabling AI-ready analysis with graph embeddings and path-based reasoning for hypothesis generation. Researchers can query the graph via SPARQL or natural language interfaces. This framework converts static data repositories into dynamic discovery environments, supporting cross-study pattern recognition, predictive modeling, and systematic exploration of genotype-phenotype relationships in Down syndrome.","short_abstract":"Trisomy 21 results in Down syndrome, a multifaceted genetic disorder with diverse clinical phenotypes, including heart defects, immune dysfunction, neurodevelopmental differences, and early-onset dementia risk. Heterogeneity and fragmented data across studies challenge comprehensive research and translational discovery...","url_abs":"https://arxiv.org/abs/2509.01565","url_pdf":"https://arxiv.org/pdf/2509.01565v1","authors":"[\"Madan Krishnamurthy\",\"Surya Saha\",\"Pierrette Lo\",\"Patricia L. Whetzel\",\"Tursynay Issabekova\",\"Jamed Ferreris Vargas\",\"Jack DiGiovanna\",\"Melissa A Haendel\"]","published":"2025-09-01T15:50:38Z","proceeding":"q-bio.QM","tasks":"[\"q-bio.QM\",\"cs.AI\",\"cs.DB\",\"cs.LG\"]","methods":"[\"LoRA\"]","has_code":false}