Milind Jagota

The paper uses allelic inclusion data to train models that identify antibody sequence constraints, finding stronger correlations with antibody properties than existing methods.

This study uses deep scanning mutagenesis to analyze POT1 mutations in human stem cells, revealing that some cancer-associated variants drive telomere elongation and oncogenesis independently of telomere replication.

This paper introduces a cross-protein transfer learning framework that improves disease variant prediction using deep mutational scanning data from few proteins, achieving better specificity than existing models.

The paper shows that protein dynamics impose evolutionary constraints and that unsupervised language models can predict these constraints from sequence data.