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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2509.01565 |
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| _version_ | 1866911132960161792 |
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| author | Krishnamurthy, Madan Saha, Surya Lo, Pierrette Whetzel, Patricia L. Issabekova, Tursynay Vargas, Jamed Ferreris DiGiovanna, Jack Haendel, Melissa A |
| author_facet | Krishnamurthy, Madan Saha, Surya Lo, Pierrette Whetzel, Patricia L. Issabekova, Tursynay Vargas, Jamed Ferreris DiGiovanna, Jack Haendel, Melissa A |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_01565 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enabling Down Syndrome Research through a Knowledge Graph-Driven Analytical Framework Krishnamurthy, Madan Saha, Surya Lo, Pierrette Whetzel, Patricia L. Issabekova, Tursynay Vargas, Jamed Ferreris DiGiovanna, Jack Haendel, Melissa A Quantitative Methods Artificial Intelligence Databases Machine Learning 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. |
| title | Enabling Down Syndrome Research through a Knowledge Graph-Driven Analytical Framework |
| topic | Quantitative Methods Artificial Intelligence Databases Machine Learning |
| url | https://arxiv.org/abs/2509.01565 |