Preetam Ghosh

This paper introduces a physics-informed machine learning framework for automatic model reduction in chemical reaction networks, enhancing efficiency and accuracy in simulating complex systems like Aβ fibril aggregation.

CORTADO identifies cell-type-specific marker genes using hill-climbing optimization, improving sensitivity and specificity over existing methods.

COFFEE improves gene regulatory network inference in scRNA-seq by integrating multiple methods through a consensus approach, outperforming individual algorithms on various datasets.

CHAI improves cell-type identification in scRNA-seq by integrating clustering results from multiple methods, outperforming existing approaches and supporting spatial transcriptomics data.

The paper examines the relationship between early life alcohol consumption and neuron-enriched exosomal miRNA expression, finding significant associations with specific miRNAs and biological functions.

The paper reclassifies strain PO100/5 as Corynebacterium silvaticum, identifies genetic markers, and shows its geographic and genetic distinctiveness from other strains.