Divyanshu Srivastava

This study develops lymphomoids from patient lymphoma samples to assess therapy response, showing consistency with clinical outcomes.

The paper introduces a graph-wavelet model to predict protein biophysical properties and assess mutational effects by analyzing amino-acid features in protein structures.

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

This paper introduces GWNet, a graph-wavelet method for gene-network inference that improves consistency in single-cell transcriptome analysis, revealing age-related regulatory changes in lung cells.

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.