Sumanta Basu

PROLONG selects metabolites co-varying with tuberculosis mycobacterial load using penalized regression with network and group constraints in longitudinal omics data.

The paper introduces an empirical Bayes method to estimate ODE models of gene expression and derives a biologically-informed similarity metric to identify co-regulated gene clusters.

The paper uses time series RNA-sequencing to analyze the female Drosophila melanogaster's transcriptional response to Sex Peptide, revealing metabolic and neuronal gene regulatory networks and interactions with the circadian clock.

The paper performs high-resolution RNA-seq time course analysis of Drosophila immune response to Imd stimulation, identifying gene expression dynamics and trade-offs with metabolic processes.

The paper identifies a class of enhancers with high prediction accuracy by focusing on regulatory heterogeneity in Drosophila, achieving 86.7% precision and 98% recall.

This paper introduces signed iRF (s-iRF), a method to refine interaction search in biological systems, identifying stable and predictive feature interactions for enhancer activity and spatial gene expression.

The paper introduces iRF, an algorithm for detecting stable high-order genomic interactions, and demonstrates its application in enhancer activity and alternative splicing.