Ajitha Rajan

This study shows that systemic FAK inhibition reduces immunosuppressive macrophages in glioblastoma by blocking monocyte trafficking, enhancing T cell activity and improving survival in a mouse model.

The paper expands the concept of MHC Class I peptide binding promiscuity to support vaccine discovery across cancers by analyzing data from 2,323 samples.

The paper proposes ConSurv, a model that balances interpretability and accuracy in cancer survival analysis using gene expression and clinical data from breast cancer patients.

This paper introduces CAPE-Beam, a decoding strategy for ProteinMPNN that reduces CTL immunogenicity risk by limiting designs to kmers not presented to CTLs or subject to central tolerance.

The paper integrates MHC Class I visibility targets into the ProteinMPNN protein design process to reduce immune visibility while preserving structure.

The paper develops a machine learning method to modify protein immune-visibility to CTLs, showing it can alter HIV Nef's visibility while preserving sequence diversity.

The paper introduces MHCXAI, an explainable AI framework to interpret deep learning predictors of MHC class I peptide presentation, validating explanations against ground truth and robustness.

The paper develops COD-dipp to map MHC Class I antigens from cancer studies and identifies immune-relevant genomic regions for vaccine design.