Vivek Das

This study shows that high glucose in kidney organoids causes inflammation and epithelial detachment, which can be reversed by targeting cytokines and signaling pathways.

The paper introduces NNclinSSOAP, a GxP-ready pipeline for processing single-cell and spatial transcriptomics data, enabling reproducible and scalable analysis in clinical settings.

NeuroMDAVIS is a multi-omics visualization model that integrates multiple data modalities to generate low-dimensional embeddings preserving local and global structures, outperforming existing methods.

This study shows semaglutide reduces neuroinflammation in mice by modulating microglia, endothelial cells, and pericytes, with implications for neurodegenerative diseases.

This study benchmarks three deconvolution methods in spatial transcriptomics for cardiovascular and kidney diseases, finding comparable performance with method-specific trade-offs.

The paper integrates single-cell RNA-seq and spatial transcriptomics data to reveal cellular interactions and changes in human atherosclerosis.

UMINT is a novel unsupervised neural network for integrating single-cell multi-omics data, achieving better performance than existing methods on CITE-seq datasets.

The paper integrates transcriptomics, proteomics, and metabolomics data from vervet monkeys to identify sirtuin signaling and PPARA network alterations in response to a high fructose diet.