Martin T. Wells

The paper introduces e3SIM, a simulator that models epidemiological, ecological, and evolutionary processes in pathogens, demonstrating its use with SARS-CoV-2 and Mycobacterium tuberculosis.

This paper introduces QCML, a quantum-inspired machine learning framework, to predict chromosomal instability from CTC morphology, achieving higher accuracy than conventional methods in high-dimensional biomedical data.

This paper develops a Bayesian framework to infer population dynamics with dormancy using molecular sequence data under the strong seedbank coalescent model.

PROLONG selects metabolites co-varying with tuberculosis mycobacterial load using penalized regression with network and group constraints in longitudinal omics data.

The paper introduces an empirical Bayes method to estimate ODE models of gene expression and derives a biologically-informed similarity metric to identify co-regulated gene clusters.

The paper uses time series RNA-sequencing to analyze the female Drosophila melanogaster's transcriptional response to Sex Peptide, revealing metabolic and neuronal gene regulatory networks and interactions with the circadian clock.

The paper performs high-resolution RNA-seq time course analysis of Drosophila immune response to Imd stimulation, identifying gene expression dynamics and trade-offs with metabolic processes.