Rintu Kutum

The paper presents an ensemble model that improves predictions of perceptual similarity between odor mixtures using a curated dataset and collaborative challenge results.

This study shows that host evolution against coinfections is influenced by the growth and virulence dynamics of individual pathogens, with fast-acting pathogens hindering adaptive success.

This study analyzes Indian genetic variations in the kinome to identify sequence, structural, and pharmacogenomic differences compared to global datasets, highlighting implications for drug safety and adverse reactions.

Strainflow predicts SARS-CoV-2 variant surges using genomic data, accurately forecasting Delta and Omicron surges and India's surge two months in advance.

The paper investigates how CDK5 phosphorylates cPLA2 in Parkinson's Disease, leading to neuroinflammation and identifies potential therapeutic targets.

The paper organizes a DREAM challenge to benchmark predictive models of single-cell signaling using mass cytometry data from breast cancer cell lines.

The paper develops predictive models for preterm birth using maternal blood multi-omics data, finding transcriptomic and proteomic markers can predict gestational age and spontaneous preterm birth with high accuracy.

The paper evaluates generative models for sample augmentation in microarray datasets, comparing VAE, GAN, and GMM in a tuberculosis dataset to improve biomarker discovery.

The paper identifies genetic variations associated with three Ayurvedic constitution types using exome sequencing and computational methods, revealing actionable SNPs and genes with pleiotropic effects.

The paper identifies methylation changes in CAD-related genes in young patients compared to controls, suggesting potential epigenetic markers for CAD risk.

This study establishes iPSC-derived neuronal cell lines for SCA12, identifies interferon signaling and inflammatory signatures as key transcriptomic features.