Leveraging TME features and multi-omics data with an advanced deep learning framework for improved C...
Leveraging TME features and multi-omics data with an advanced deep learning framework for improved Cancer survival prediction
About this item
Full title
Author / Creator
Fan, Xuan , He, Zihao , Guo, Jing , Bu, Dechao , Han, Dongchen , Qu, Xinchi , Li, Qihang , Cheng, Sen , Han, Aiqing and Guo, Jincheng
Publisher
London: Nature Publishing Group UK
Journal title
Language
English
Formats
Publication information
Publisher
London: Nature Publishing Group UK
Subjects
More information
Scope and Contents
Contents
Glioma, a malignant intracranial tumor with high invasiveness and heterogeneity, significantly impacts patient survival. This study integrates multi-omics data to improve prognostic prediction and identify therapeutic targets. Using single-cell data from glioblastoma (GBM) and low-grade glioma (LGG) samples, we identified 55 distinct cell states vi...
Alternative Titles
Full title
Leveraging TME features and multi-omics data with an advanced deep learning framework for improved Cancer survival prediction
Authors, Artists and Contributors
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Primary Identifiers
Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_1511d0ef2857406eb16a8213977711e2
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_1511d0ef2857406eb16a8213977711e2
Other Identifiers
ISSN
2045-2322
E-ISSN
2045-2322
DOI
10.1038/s41598-025-98565-0
