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A Human Pan-Disease Whole Blood Transcriptomics Atlas Reveals Systemic Signatures Across Diseases

Mardinoglu, Adil, Li, Mengzhen
May 10, 2026
Published Date

Research Abstract & Technology Focus

This dataset accompanies the manuscript titled “A Human Pan-Disease Whole Blood Transcriptomics Atlas Reveals Systemic Signatures Across Diseases” Whole-blood transcriptomics (WBT) provides critical insights into systemic health and disease. In this study, we established a large-scale WBT Atlas comprising 4,444 samples across 98 distinct health conditions. Through integrative analyses, we identified disease-specific gene expression signatures and developed a multi-omics classification framework capable of distinguishing among these conditions based on their unique transcriptomic profiles. The dataset includes RNA-seq data from 4,444 samples used for atlas construction, along with 10 cohorts utilized for machine learning–based external validation.
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A Human Pan-Disease Whole Blood Transcriptomics Atlas Reveals Systemic Signatures Across Diseases

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This literature focuses on: This dataset accompanies the manuscript titled “A Human Pan-Disease Whole Blood Transcriptomics Atlas Reveals Systemic Signatures Across Diseases” Whole-blood transcriptomics (WBT) provides critical insights into systemic health and disease. In th...

What other academic literature is closely related to 'A Human Pan-Disease Whole Blood Transcriptomics Atlas Reveals Systemic Signatures Across Diseases'?

Yes, highly correlated activity was mapped. An entry titled 'A Human Pan-Disease Whole Blood Transcriptomics Atlas Reveals Systemic Signatures Across Diseases' discusses this: This dataset accompanies the manuscript titled “A Human Pan-Disease Whole Blood Transcriptomics Atlas Reveals Systemic Signatures Across Diseases” ...

Are there commercial applications of 'A Human Pan-Disease Whole Blood Transcriptomics Atlas Reveals Systemic Signatures Across Diseases' in market news publications?

Yes, highly correlated activity was mapped. An entry titled 'Generative AI for misalignment-resistant virtual staining to accelerate histopathology workflows' discusses this: Ma, Li, and colleagues present a virtual tissue staining method that overcomes data mismatch by separating image generation from spatial alignment....

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