Colleen Kenost

"Single-subject studies"-derived analyses unveil altered biomechanisms between very small cohorts: implications for rare diseases

The paper introduces Inter-N-of-1, a method to detect biomechanism differences in small cohorts by analyzing single-subject transcriptome data, showing high precision and recall in simulations and a clinical dataset.

binomialRF: Scalable Feature Selection and Screening for Random Forests to Identify Biomarkers and Their Interactions

binomialRF identifies biomarkers and their interactions using a scalable random forest approach with statistical hypothesis testing for feature selection.

Evaluating single-subject study methods for personal transcriptomic interpretations to advance precision medicine

The paper evaluates single-subject methods for differential gene expression analysis and finds ensemble methods perform best in identifying DEGs without replicates.

Interpretation of Omics dynamics in a single subject using local estimates of dispersion between two transcriptomes

The paper introduces iDEG, a method to identify differentially expressed genes in a single subject without replicates, showing improved accuracy over existing methods in simulations.