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Establishment of interpretable cytotoxicity prediction models using machine learning analysis of transcriptome features

GSE252529 Homo sapiens (human) RNA-seq (gene expression) 15 samples Submitted 2025/05/28 Platform GPL24676

At a glance

RNA-seq (gene expression) · human · 15 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: 15 samples: 6 conditions, mostly in triplicateGroups 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: Establishment of interpretable cytotoxicity prediction models using machine learning analysis of transcriptome featuresActa pharmaceutica Sinica. B · 2025

Summary (written by the submitters)

Cytotoxicity, usually represented by cell viability, is a crucial parameter for evaluating drug safety in vitro. Accurate prediction of cell viability/cytotoxicity could accelerate drug development in the early stage. In this study, by using machine learning algorithms on cellular transcriptome and cell viability data, highly accurate prediction models of 50% and 80% cell viability were developed with AUROCs of 0.90 and 0.84, respectively, which also showed good performance on diverse cell lines.

Read the full summary ↓

With respect to the characterization of Feature Genes employed, the models can be interpreted, and the mechanisms of bioactive compounds with narrow therapeutic indices can also be analyzed. In summary, the models established in this study have the capacity to predict cytotoxicity highly accurately across cell lines and can be used for high safety substances screening efficiently. Moreover, the Cytotoxicity Signature genes from interpretability analysis is valuable for studying the mechanisms of action, especially for substances with narrow therapeutic indices.

Published in

Establishment of interpretable cytotoxicity prediction models using machine learning analysis of transcriptome features

Wu Y, Tang K, Wang C et al. · Acta pharmaceutica Sinica. B 2025 · PMID 40370539 · doi:10.1016/j.apsb.2025.02.009

This dataset

Open GSE252529 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 GSE252529_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 PRJNA1061122 and SRA study SRP481460. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40370539) ↗

Study design

6 conditions, mostly in triplicate

A549_DMSO ×3 HepG2_DMSO ×3 HT29_DMSO ×3 A549_Triptolide ×2 HepG2_Triptolide ×2 HT29_Cyclosporine ×2

Supports a between-group comparison across 15 samples.

How we read this: 6 replicated groups read from 15 sample titles; they account for 15 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

GSM8002601 A549_DMSO_Rep1 A549_DMSO
GSM8002602 A549_DMSO_Rep2 A549_DMSO
GSM8002603 A549_DMSO_Rep3 A549_DMSO
GSM8002604 A549_Triptolide_Rep1 A549_Triptolide
GSM8002605 A549_Triptolide_Rep2 A549_Triptolide
GSM8002606 HepG2_DMSO_Rep1 HepG2_DMSO
GSM8002607 HepG2_DMSO_Rep2 HepG2_DMSO
GSM8002608 HepG2_DMSO_Rep3 HepG2_DMSO
GSM8002609 HepG2_Triptolide_Rep1 HepG2_Triptolide
GSM8002610 HepG2_Triptolide_Rep2 HepG2_Triptolide
GSM8002611 HT29_DMSO_Rep1 HT29_DMSO
GSM8002612 HT29_DMSO_Rep2 HT29_DMSO
GSM8002613 HT29_DMSO_Rep3 HT29_DMSO
GSM8002614 HT29_Cyclosporine_Rep1 HT29_Cyclosporine
GSM8002615 HT29_Cyclosporine_Rep2 HT29_Cyclosporine

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