Purva Kulkarni

The paper identifies major sources of variability in iPSC-derived neurons and shows how correcting these improves disease signature detection.

The paper presents a comprehensive multi-omics dataset of healthy individuals, revealing sex and age influences and providing reference ranges for omics features.

The paper develops a plasma N-glycoproteomics method to identify high-specificity disease biomarkers by detecting site-specific glycosylation abnormalities in genetic diseases.

The paper develops a multi-omics data analysis workflow packaged as a FAIR Digital Object to improve reproducibility and reuse in computational research.

The paper identifies novel biomarkers for pyridoxine-dependent epilepsy using metabolomics and infrared ion spectroscopy, showing their potential for newborn screening and clinical implications.