Eric J. Deeds

The paper quantifies developmental variation in C. elegans natural populations using embGAN, finding genotype-specific differences in cell-cycle timing that exceed within-strain variation.

The paper shows that collagen abundance alone does not define fibrosis in DMD, as distinct matrix states with different biological activities and mechanosignaling exist.

Comparing local point neighborhoods before and after dimensionality reduction reveals that standard scRNA-seq pipelines, t-SNE, and UMAP often distort local neighborhoods by more than 95%, altering downstream clustering.

This study identifies sex differences in tubuloglomerular feedback dynamics in rodent kidneys, showing males have higher oscillation frequencies but are more prone to TGF loss, leading to increased injury risk.

DeepAtlas generates local manifold embeddings and assesses the manifold hypothesis, finding that many datasets, including single-cell RNA-sequencing, do not conform to it.

The paper introduces a binomial model to identify biologically variable genes in scRNA-seq data, showing it outperforms existing methods in preserving biological structure.

The paper develops a mathematical model of stacked trimer assembly, revealing that strong interaction affinities can lead to kinetic trapping, suggesting evolutionary pressure for weaker interactions in stacked ring structures.

This paper introduces a new metric called branch distance to analyze developmental phenotypes in C. elegans embryos, revealing previously unnoticed heterogeneity and links between cell fate and cell cycle pathways.

This paper challenges the idea that distinct cell types correspond to attractors in gene expression space by showing cells of different types occupy the same regions in epigenetic space.

This study shows that protein turnover reduces the switch-like behavior of PTM cycles and highlights the impact of E3 ligase crosstalk on protein expression changes.

The paper introduces a machine learning classifier for virtual screening that reduces false positives by using a novel training dataset and shows promising results in identifying active compounds.

The paper investigates how COX-2 processes multiple input signals in a complex network, finding that input variability and correlation affect information transmission and that COX-2 acts as a signal integrator.

This study uses dynamical models to examine scaffold-mediated signaling and finds that scaffold function depends on assembly paradigms, with implications for therapeutic targeting and synthetic biology.