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WBT-DC Pipeline: Whole Blood Transcriptomics data-based Disease Classification

GSE282218 Homo sapiens (human) RNA-seq (gene expression) 165 samples Submitted 2026/05/20 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 165 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Homo sapiens (human)
Samples
165 samples: 104 × P001 vs 45 × P00108 vs 16 × P00109Groups are read from the sample names, not from the paper. Check the list below.
Data you can get
A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Paper
WBT-DC pipeline: a cross-cohort and cross-platform disease classification pipeline based on whole-blood transcriptomicsJournal of translational medicine · 2026
Summary (written by the submitters)
Machine learning together with cell/tissue transcriptomics data has been widely used for disease classification. However, obtaining transcriptomics data for human tissues require invasive procedures, making it challenging for widespread application in the clinic. In this study, we developed the WBT-DC (Whole Blood Transcriptomics (WBT) data based Disease Classification).
Read the full summary ↓
We utilized gene rank-based methods for feature extraction to mitigate issues associated with batch effects and gene noise. We applied the ensemble machine learning model, Random Forest, and performed cross-validation and model tuning. We evaluated our methods on four different diseases including crohn's disease (CD), ulcerative colitis (UC) and amyotrophic lateral sclerosis (ALS) and rheumatoid arthritis (RA) datasets, using data from seven independent cohorts and 2,452 participants, across RNA-Sequencing and microarrays. Our machine learning based WBT-DC pipeline demonstrated a robust performance across various disease datasets and different transcriptomics platforms, establishing itself as a valuable non-invasive tool for future disease classification and prediction.
Published in
WBT-DC pipeline: a cross-cohort and cross-platform disease classification pipeline based on whole-blood transcriptomics
Li M, Jin H, Meng L et al. · Journal of translational medicine 2026 · PMID 42116144 · doi:10.1186/s12967-026-08254-3
This dataset
Open GSE282218 in finder → Find more human RNA-seq datasets →
Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

Series matrix ↓
2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE282218_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

Raw reads on ENA ↗

What the files usually look like: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1187601 and SRA study SRP546017. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42116144) ↗
Study design
104 × P001 vs 45 × P00108 vs 16 × P00109

Supports a between-group comparison across 165 samples.

How we read this: 3 replicated groups read from 165 sample titles; they account for 165 of them. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.

Samples in this study

+ 125 more — browse all 165 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.