Alicia Lundby

This paper evaluates statistical methods for analyzing repeated-measures proteomics data, finding that unstructured covariance models outperform conventional approaches in handling variance heterogeneity and within-subject correlations.

This study investigates factors influencing HCM phenotype penetrance by comparing MYH7 mutations in isogenic hiPSC-CMs and shows that beta-adrenergic stimulation and gene dosage affect disease expression.

The paper introduces a tool to optimally allocate samples to batches to balance covariates and reduce technical variability impacts.

The paper develops a high-throughput workflow for subcellular proteome and phospho-proteome profiling and applies it to study EGFR signaling and stress responses in cells.

The paper compares cardiac protein profiles across species to identify suitable models for human cardiac diseases.