From bulk to single-cell and spatial data: An AI framework to characterise breast cancer metabolic dysregulations across modalities
Open Access
- 18 October 2025
- journal article
- Published by Elsevier in Computers in Biology and Medicine
- Vol. 198 (Pt B) , 111195
Abstract
No abstract availableKeywords
Funding Information
- Engineering and Physical Sciences Research Council (EP/Y001613/1)
- Alan Turing Institute (D-ELA-013)
- Alan Turing Institute (TNDC2-100022)
- HORIZON EUROPE Framework Programme (101182278)
- Biotechnology and Biological Sciences Research Council (BB/Y01278X/1)
- Biotechnology and Biological Sciences Research Council (BB/X017745/1)
- Biotechnology and Biological Sciences Research Council (NBIC_FTMA3_21_013)
- Teesside University
- National Institute of Technology Warangal
This publication has 88 references indexed in Scilit:
- Finding a needle in a haystack: insights on feature selection for classification tasksJournal of Intelligent Information Systems, 2023
- RAB5C, a new mRNA binding target of HuR, regulates breast cancer cell proliferationCell Biology International, 2022
- Metabolic changes in triple negative breast cancer-focus on aerobic glycolysisMolecular Biology Reports, 2021
- LncRNA ZFAS1 inhibits triple-negative breast cancer by targeting STAT3Biochimie, 2021
- Breast cancer early detection: A phased approach to implementationCancer, 2020
- In Silico Prediction of Metabolic Epoxidation for Drug‐like Molecules via Machine Learning MethodsMolecular Informatics, 2020
- Detection of Skin Cancer Using SVM, Random Forest and kNN ClassifiersJournal of Medical Systems, 2019
- The GeneCards Suite: From Gene Data Mining to Disease Genome Sequence AnalysesCurrent Protocols in Bioinformatics, 2016
- Genome‐scale modeling of human metabolism – a systems biology approachBiotechnology Journal, 2013
- The prognostic significance of WWOX expression in patients with breast cancer and its association with the basal-like phenotypeJournal of Cancer Research and Clinical Oncology, 2010