Sharmila S. Mande

The paper applies natural language processing techniques to SARS-CoV-2 genomes to identify mutation signatures and track semantic changes associated with variants of concern.

The paper predicts growth patterns and symbiotic relationships of 803 gut microbes across 13 diets using metabolic models.

The paper uses machine learning to classify SARS-CoV-2 variants by mutation patterns and identifies key mutations associated with disease severity outcomes.

The paper applies Latent Dirichlet Allocation to SARS-CoV-2 genomes treated as documents, identifying 16 amino acid and 18 nucleotide mutation signatures and their global distribution.