Awanti Sambarey

TL;DR: A computational pipeline is described that adopts an unbiased approach to identify a biomarker signature comprising 10 genes that can discriminate between TB and healthy controls as well as distinguish TB from latent tuberculosis and HIV in most cases.

TL;DR: A meta-analysis of host’s whole blood transcriptomic profiles that were integrated into a genome-scale protein–protein interaction network to generate response networks in active tuberculosis, and reports the emergence of a highly active common core in disease, showing partial reversals upon treatment.

TL;DR: This work constructs a comprehensive network of infection-related processes in a human macrophage comprising 1888 proteins and 14,016 interactions and uses a novel formulation for mining response networks that has led to identifying highest activities in the cell.

TL;DR: The study concludes that altered balance in the levels of serum cytokines can be indicative of TB pathogenesis and profiling of dynamic changes in cytokines would facilitate effective TB diagnostic and treatment strategies.

TL;DR: In this paper, a machine learning model, INDIGO-MTB, was used to predict in vitro drug interactions using drug transcriptomics, with a multi-scale model of drug PK/PD and pathogen-immune interactions called GranSim.