Jamie L. Inman

This study uses volume EM to reveal the three-dimensional structure of desmosomes in epithelial cells and identifies novel features like discontinuities in the outer dense plaque.

The paper presents two large-scale proteomics datasets from mice exposed to varying X-ray doses and time points, enabling biomarker discovery and machine learning applications for radiation exposure assessment.

The paper develops a genome-based framework to predict bacteriophage infectivity against diverse Pseudomonas aeruginosa strains using machine learning and experimental data.

This study investigates whether protein quantification and physical normalization are necessary in proteomics by comparing normalized and unnormalized sample inputs, showing that computational normalization can reduce variability in some cases.

The paper identifies a mouse tumor susceptibility gene signature (mTSGS) that predicts chemotherapy response in ERBB2-driven breast cancer, validated in both mouse models and human datasets.