Kumar Selvarajoo

The paper uses synthetic data to model transcriptional regulation of differentially expressed genes in ovarian cancer, identifying genes with altered regulatory dynamics.

The paper introduces a gene-level divergence score to quantify differences between bulk and single-cell transcriptomics in breast cancer and other tissues, identifying genes that systematically differ between platforms.

The paper analyzes RNA-Seq data of CLL cells to identify toggle genes and temporal differentially expressed genes, showing their role in cell-state transitions and population plasticity.

This study uses machine learning to compare bulk and pseudo-bulk RNA-seq datasets, finding significant differences in analytic metrics and biological processes.

This study evaluates evolutionary algorithms for estimating reaction kinetic parameters under different noise conditions and identifies algorithm-specific effectiveness.

The paper shows that aging and senescence increase transposon expression through transcriptional readthrough and intron retention, not autonomous transposon activity.

The paper develops a cellular automata model to simulate Pseudomonas aeruginosa biofilm formation and shows that azithromycin delays but does not prevent biofilm formation by affecting single-cell motility.

The paper uses a non-linear Kuramoto model to show that scale-free networks enable synchronization in biological systems, suggesting low expression genes are key for biofilm phase transitions.

ABioTrans is an R-based web tool for transcriptomics analysis that provides statistical methods and visualization for RNA-Seq data from GEO.

The paper analyzes transcriptome-wide expression patterns in Saccharomyces cerevisiae biofilm strains, finding global regulatory stability despite local expression changes.