A pioneering multi-modal framework with TCR-peptide-HLA sequence and structure features incorporating an attention mechanism designed to accurately identify tumor antigens with immunogenic properties, which presents a brand-new and promising approach for cancer immunotherapies that target tumor antigens.
The ImmunoFoundation Model (IFM), a multimodal deep learning system that integrates not only peptide sequences, 3D molecular structures, and biochemical properties but also TCR-MHC-peptide to achieve superior immunogenicity prediction and enable peptide optimization for therapeutic applications is developed.
Smita Krishnaswamy, J. Rocha, Hiren Madhu et al.· Journal of Immunology· 0 citations
This work is the first to use a multi-modal foundation model for neoantigen vaccines, overcoming the tumor-type-specific constraints of conventional approaches and facilitating joint modelling of immunogenicity and clinical efficacy, thereby offering an AI decision engine for precision cancer vaccine design that is app...
Gang Liu, Jia Wang, Jia Zhu· Global Health Care· 2 citations
The current role of AI is summarized across the peptide cancer vaccine development pipeline, from neoantigen discovery and epitope prioritization to prediction of peptide–HLA binding, antigen presentation, and T-cell receptor recognition, and the application of modern computational frameworks.
P. Brlek, J. Kolić, L. Bulić et al.· Frontiers in Genetics· 0 citations
AAMFM, an Antigen-specific Antibody Multimodal Foundation Model that learns unified representations of antibody sequences and structures conditioned on antigen context, achieves state-of-the-art performance in functional antibody design, revealing its potential for antigen-specific antibody engineering.
Xiaoliang Shi, Zichen Wang, Runze Ma et al.· arXiv.org· 0 citations
Breast cancer, particularly hormone receptor-positive (HR+) and triple-negative (TNBC) subtypes, is often immunologically "cold," limiting immunotherapy efficacy. Neoantigen-based vaccines hold promise but face challenges from inaccurate identification and overreliance on HLA binding affinity, which poorly captures tru...
Van-The Le, Juan Peter Timothy Yuune, Jiun-I Lai et al.· Biochemical and Biophysical...· 0 citations
Evaluation across multiple settings indicates that TCR-IFNet achieves competitive performance compared with existing methods, with higher AUPRC observed on both antigen-specific and healthy-sourced datasets, as well as improved results on independent test sets.
Wen-Yu Xi, Ru-Heng Wang, Xiu-Cai Ye et al.· International Journal of Bio...· 0 citations
Learn how Google DeepMind and GovTech Singapore uses lightweight on-device Gemma models to intercept conversational scams in private chats with zero cloud data egress.
MIT News · Artificial Intelligence· news.mit.eduOct 13, 2026
Machine-learning networks can learn without detailed instructions from a designer. Those that do may offer insight into how the brain reorganizes itself during learning.
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