From bulk to single-cell and spatial data: An AI framework to characterise breast cancer metabolic dysregulations across modalities
Published: 27 October 2025| Version 1 | DOI: 10.17632/z53vvrwg74.1
Contributors:
, Annalisa OcchipintiDescription
The data folder contains the data files associated with the publication Le Minh Thao Doan, Suraj Verma, Noushin Eftekhari, Claudio Angione, Annalisa Occhipinti. "From bulk to single-cell and spatial data: An AI framework to characterise breast cancer metabolic dysregulations across modalities" Computers in Biology and Medicine, Volume 198, Part B, 2025, 111195, ISSN 0010-4825. DOI: https://doi.org/10.1016/j.compbiomed.2025.111195 This repository contains: - raw (clinical_Original.csv) and preprocessed clinical data (clinical_data.csv) - raw transcriptomic data (TCGA-noise-ENSG.csv) - breast Gtext data (Gtex-noiso-ENSG.csv) - fluxomic data generated from genome-scale metabolic model (flux-test.csv)
Files
Institutions
- Teesside University
Categories
Breast Cancer, Machine Learning, Cell Metabolism, Single-Cell RNA Sequencing, Spatial Transcriptomics