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openalexZenodo (CERN European Organization for Nuclear Research)Cited by 0

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

Mardinoglu, Adil, Li, Mengzhen

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.

Also available via: European Organization for Nuclear Research

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openalexZenodo (CERN European Organization for Nuclear Research)2026-07-26

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openalexZenodo (CERN European Organization for Nuclear Research)2026-07-24

Dataset: SOD1 Research July 2026 - PathMap Experiment #000084

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openalexZenodo (CERN European Organization for Nuclear Research)2026-08-15

Deep Learning for Human Activity Recognition: A Comprehensive Review of Architectures, Performance, and Challenges Across Five Sensory Datasets

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Also available via: European Organization for Nuclear Research

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openalexZenodo (CERN European Organization for Nuclear Research)2026-07-25

Development of a Convolutional Neural Network-Based Web Application for Automated Skin Disease Classification

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openalexZenodo (CERN European Organization for Nuclear Research)2026-07-23

Integrating ToF-SIMS and machine learning reveals a mammary tumor-associated multi-ion signature — analysis code

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openalexZenodo (CERN European Organization for Nuclear Research)2026-07-27

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