Martin T. Wells

e3SIM: epidemiological-ecological-evolutionary simulation framework for genomic epidemiology

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.

Quantum Cognition Machine Learning for Forecasting Chromosomal Instability

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.

Bayesian phylodynamic inference of population dynamics with dormancy

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

PROLONG: Penalized Regression for Outcome guided Longitudinal Omics analysis with Network and Group constraints

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

An Empirical Bayes Approach to Estimating Dynamic Models of Co-regulated Gene Expression

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.

Time series transcriptome analysis uncovers regulatory networks and a role for the circadian clock in the Drosophila melanogaster female's response to Sex Peptide

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.

Dense time-course gene expression profiling of the Drosophila melanogaster innate immune response

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.