HR-VILAGE-3K3M: A Human Respiratory Viral Immunization Longitudinal Gene Expression Dataset for Systems Immunity
arXiv:2505.14725v2 Announce Type: replace-cross Abstract: Respiratory viral infections pose a global health burden, yet the cellular immune mechanisms underlying protection and pathology remain unclear. Natural infection cohorts often lack pre-exposure baselines and time-controlled sampling, whereas inoculation and vaccination trials generate well-structured longitudinal transcriptomic data. However, these datasets are scattered across repositories and processed inconsistently, hindering integrative and AI-driven analyses. To address these challenges, we developed the Human Respiratory Viral I
The proliferation of disparate biological datasets and advances in AI and 'systems immunity' are converging, necessitating integrated resources for advanced analysis.
This initiative provides a standardized, AI-ready dataset crucial for understanding host immune responses to respiratory viruses, which has direct implications for vaccine development and personalized medicine.
The availability of a harmonized, large-scale longitudinal gene expression dataset will accelerate AI-driven research in immunology, potentially leading to faster insights into viral immunity and pathology.
- · AI-driven drug discovery companies
- · Immunology researchers
- · Vaccine developers
- · Public health organizations
- · Fragmented bioinformatics pipelines
- · Traditional drug discovery methods
Artificial intelligence applications in personalized medicine for infectious diseases will become more robust and effective.
Improved predictive models for viral infection outcomes and vaccine efficacy could lead to more targeted public health interventions.
The success of this data integration model could spur similar initiatives across other biological and medical domains, accelerating synthetic biology applications.
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Read at arXiv cs.LG