Nicholas L. Panchy

The paper compares dose- and time-dependent EMT transcriptomes to show stable intermediate states and improved gene detection in dose-dependent experiments.

This study uses deep learning to accurately measure Arabidopsis fitness traits, revealing gene function effects previously undetected by traditional methods.

This study introduces gsPCA and gsNMF for analyzing transcriptome data, showing they provide interpretable, transferrable, and gene-specific functional insights comparable to existing methods.

The paper develops a mathematical model to explain how eS6 phosphorylation integrates circadian and diel signals to detect daylength changes.

The paper uses combinatorial perturbations to study EMT gene regulation, finding that mesenchymal genes show diverse dependencies on TGF-β and ZEB1, with functional differences in motility and cancer survival.

The paper predicts cell-cycle expressed genes in S. cerevisiae using regulatory data, identifying both canonical and non-canonical regulators of time-specific expression.

The paper identifies intergenic transcribed regions (ITRs) in 15 flowering plant species and uses machine learning to classify them as functional or non-functional based on sequence and evolutionary features.

The paper investigates why transcription factor duplicates are retained after whole genome duplication in Arabidopsis, finding that partitioning of ancestral expression and regulatory sites between duplicates contributes to their retention.