A trustworthy semantic knowledge network linking data-driven cell phenotypes to diseases and drugs
- 1Division of Intramural Research, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
Abstract
Genome-wide association and other genetic studies have identified genes involved in disease pathogenesis, and their encoded proteins have provided therapeutic targets. However, little is known about which cells are the functional home of these disease-associated genes and proteins. Single-cell technologies are enabling high-resolution profiling of cell phenotypes and revealing cellular contexts of disease, yet much of the resulting knowledge remains embedded in individual datasets and scientific literature. To address these gaps, the National Library of Medicine has developed Cell Knowledge Network (NLM-CKN, https://nlm-ckn.org). Reproducible computational workflows have generated cell phenotype knowledge for 2,585 cell sets and 5,139 selectively expressed genes from 88 datasets covering 10 human organs. Mapping of these cell sets to cell types grounds Cell Ontology terms in experimental evidence. This experimental knowledge is integrated with external knowledge of anatomical and cell-type hierarchies, gene-disease associations, and therapeutic interventions. A semantic schema defines node types and relations, enabling knowledge to be transformed into triple assertions and loaded into an ArangoDB database with provenance and quantitative evidence. The NLM-CKN interface supports graphical exploration of the cellular constituents of healthy human tissues. Finally, topological analyses generate insights into cellular contributions to disease pathogenesis, cellular contexts for therapeutic intervention, and drug-repurposing hypotheses.
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BibTeX
@misc{xu2026trustworthy,
title = {{A trustworthy semantic knowledge network linking data-driven cell phenotypes to diseases and drugs}},
author = {B. Xu and A. Deslattes Mays and M. Diller and R. LeClair and A. V. Pankajam and W. Spear and D. Osumi-Sutherland and K. Arvind and S. Chatterjee and A. D. Diehl and M. M. Islam and C. Kelly and R. Khalil and J. Khare and R. Lopez Candelaria and B. Peng and L. McDonald and D. Pintard and N. Rotenberg and D. Zweibaum and Y. Zhang and R. H. Scheuermann},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.30.763199},
url = {https://www.biorxiv.org/content/10.64898/2026.10.30.763199v1},
note = {CC0},
}
RIS
TY - UNPB
TI - A trustworthy semantic knowledge network linking data-driven cell phenotypes to diseases and drugs
AU - B. Xu
AU - A. Deslattes Mays
AU - M. Diller
AU - R. LeClair
AU - A. V. Pankajam
AU - W. Spear
AU - D. Osumi-Sutherland
AU - K. Arvind
AU - S. Chatterjee
AU - A. D. Diehl
AU - M. M. Islam
AU - C. Kelly
AU - R. Khalil
AU - J. Khare
AU - R. Lopez Candelaria
AU - B. Peng
AU - L. McDonald
AU - D. Pintard
AU - N. Rotenberg
AU - D. Zweibaum
AU - Y. Zhang
AU - R. H. Scheuermann
PY - 2026
DA - 2026/10/30
PB - bioRxiv
DO - 10.64898/2026.10.30.763199
UR - https://www.biorxiv.org/content/10.64898/2026.10.30.763199v1
AB - Genome-wide association and other genetic studies have identified genes involved in disease pathogenesis, and their encoded proteins have provided therapeutic targets. However, little is known about which cells are the functional home of these disease-associated genes and proteins. Single-cell technologies are enabling high-resolution profiling of cell phenotypes and revealing cellular contexts of disease, yet much of the resulting knowledge remains embedded in individual datasets and scientific literature. To address these gaps, the National Library of Medicine has developed Cell Knowledge Network (NLM-CKN, https://nlm-ckn.org). Reproducible computational workflows have generated cell phenotype knowledge for 2,585 cell sets and 5,139 selectively expressed genes from 88 datasets covering 10 human organs. Mapping of these cell sets to cell types grounds Cell Ontology terms in experimental evidence. This experimental knowledge is integrated with external knowledge of anatomical and cell-type hierarchies, gene-disease associations, and therapeutic interventions. A semantic schema defines node types and relations, enabling knowledge to be transformed into triple assertions and loaded into an ArangoDB database with provenance and quantitative evidence. The NLM-CKN interface supports graphical exploration of the cellular constituents of healthy human tissues. Finally, topological analyses generate insights into cellular contributions to disease pathogenesis, cellular contexts for therapeutic intervention, and drug-repurposing hypotheses.
ER -