MSIPep prioritizes tumor peptides in leukemia and solid-tumor datasets
MSIPep combines immunopeptidomics, transcriptomics and deep learning to identify and computationally prioritize candidate tumor peptides in leukemia and solid-tumor datasets.
MSIPep combines immunopeptidomics, transcriptomics and deep learning to identify and computationally prioritize candidate tumor peptides in leukemia and solid-tumor datasets.
A new study finds that calreticulin, long considered an essential chaperone for MHC class I antigen presentation, is functionally expendable ...
A new review in Nature Reviews Cardiology lays out a framework for defining heart-specific T cell responses and the cardiac ...
Using mass spectrometry-based immunopeptidomics, researchers identified 57 MHC-II peptides from 23 Francisella tularensis proteins and pinpointed ten immunogenic CD4 T ...
A landmark Perspective in Nature Reviews Cancer argues that the cancer dark proteome — thousands of uncharacterized proteins from non-canonical ...
New research shows that T cell responses against endogenous retroviral peptides, particularly murine leukemia virus fragments, are critical for checkpoint ...
A new review details how artificial intelligence is being woven into every stage of personalized neoantigen cancer vaccine development, from ...
The concept of a proximity antigen sits at an important intersection of tumour immunology, spatial biology, and machine learning, and ...
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© 2025 Scienmag - Science Magazine