Neeraja M. Krishnan

X-Plat transforms expression and methylation data between microarray and sequencing platforms using polynomial regression, achieving lower RMSE than existing methods across multiple species.

Fruit-In-Sight uses deep learning to predict secondary metabolite concentration classes from fruit and leaf images, achieving high accuracy without lab equipment.

The paper provides a framework for molecular cancer subtyping that integrates quality control, normalization, batch correction, classification, and reporting, with a use case in breast cancer.

The paper identifies specific chloroplast photosystem gene adaptations in Nepenthes khasiana, suggesting molecular changes related to cyclic electron transport in carnivorous plants.

The paper uses image-based deep learning to predict intracellular metabolite concentration classes in plants, achieving 70% accuracy for fruit images.

The paper identifies 225 genes and pathways, including TGF-β and HSF-1, affected by neem extract in head and neck squamous cell carcinoma using shRNA screening and gene expression analysis.

The paper compares toxin-associated gene homologs in Daboia russelii using whole-genome sequencing data from skin exuviate, identifying unique sequence motifs and structural residues across venom domains.

RNAtor is an Android app that helps biologists design RNA-seq experiments by providing recommendations based on simulations and real data.