A-S.B.69: DataMap: Enabling Data-Driven Discovery through a Curated Biological Knowledge Graph
Amonida Zadissa
UK DRI
Caleb Webber
UK DRI
Shayan Ebrahimi
UK DRI
Nikolai Hecker
UK DRI
Aaron Pangilinan
UK DRI
Dammy Shittu
UK DRI
Keywords
Knowledge Graph
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Understanding complex diseases requires integrating biological knowledge across multiple scales, from molecular interactions to disease phenotypes. However, these relationships are often fragmented across data modalities, limiting the ability to interpret how entities such as genes, proteins, drugs, pathways, and traits interact within a coherent biological framework.
We present DataMap, a knowledge graph designed to model and contextualise relationships between biological entities relevant to neurodegenerative diseases. DataMap connects diverse modalities including SNPs, eQTLs, genes, proteins, pathways, and disease traits into a unified, biologically meaningful network. By explicitly structuring these relationships and preserving their biological context, the platform enables users to trace how molecular signals propagate across different levels of biological organisation and identify points of convergence across datasets.
DataMap supports multiple analytical scenarios, including gene-centric and drug-centric exploration. It also enables comparative analysis through a gene set connectivity approach, where relationships between two gene sets are visualised within the network to highlight clustering and shared biological context. In addition, DataMap incorporates an LLM-based interface, GeneTalk, allowing users to query the knowledge graph using natural language and interactively explore network-derived insights. Each query is supported by statistical frameworks, including gene set enrichment approaches such as MAGMA and background gene set calculations, enabling robust interpretation.
Built as a scalable, cloud-native platform with a microservices architecture, DataMap provides an interactive web interface for efficient querying and visualisation of complex biological networks. By combining integrative modelling, statistical analysis, and intuitive exploration, DataMap enables interpretable, data-driven hypothesis generation and target prioritisation in neurodegenerative diseases research.
Co-authors: Sadegh Abadijou, Nikolai Hecker, Elcid Aaron Pangillinan, Dammy Islamiat Shittu, Shayan Ebrahimi, Caleb Webber, Amonida Zadissa
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