ArticleBMC cancer2025
Peripheral blood multimodal integration via cross-attention for cancer immune profiling.
Article in BMC cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Sequence determinant and functional relevance of 8-oxoguanine RNA modification unveiled from foundation-model-based predictor.Molecular therapy. Nucleic acids · 2026Article
- Integrative interpretable learning reveals shared patterns of epitranscriptomic regulation across multiple cancer types.BMC biology · 2026Article
- Exploring the Expression Changes of Immune Genes in Alzheimer's Disease: A Study on ANXA1, VCAM1, and CCL2.Molecular neurobiology · 2026Article
- DeepSGE: predicting spatial gene expression using residual network with efficient channel attention and dynamic graph attention network.BMC genomics · 2026Article
- Multi-view knowledge-guided flow subgraphs with substructure initialization for explainable DDI prediction.Briefings in functional genomics · 2026Article
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Authors and funding
6 authors.
Funding
Abstract
objectiveAccurate cancer risk prediction is hindered by complex, multi-layered immune interactions, and traditional tissue biopsies are invasive and lack scalability for large-scale or repeated assessments. Peripheral blood offers a minimally invasive and accessible alternative for immune profiling. This study aims to develop CAMFormer, a deep learning framework that integrates multimodal peripheral blood-derived immune features for precise, non-invasive early cancer risk prediction.
methodsCAMFormer combines mRNA expression, immune cell frequencies, and TCR diversity index, leveraging a cross-attention-based multimodal Transformer to capture cross-scale immune interactions.
resultsIn five-fold cross-validation, CAMFormer achieved an AUC of 0.92 and an F1-score of 0.85 on the validation set, outperforming unimodal and baseline methods.
conclusionThese results highlight the potential benefits of integrating multimodal immune features with cross-attention mechanisms for early cancer detection and for guiding future personalized immunotherapy studies.
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