Alexander Tong

Fluxion generates enzyme function predictions by modeling electron flow trajectories, enabling function generation beyond static classification.

The paper shows that sampling biases in negative regulatory interactions inflate performance in gene regulatory network inference and proposes a degree-aware sampling approach to address this issue.

This study shows that beta cells drive pancreatic ductal adenocarcinoma development through endocrine-exocrine signaling, with obesity enhancing this process via CCK+ beta cell adaptation.

geneRNIB provides a benchmark for gene regulatory network inference, showing simpler models often outperform complex ones in perturbation-informed metrics.

This paper introduces a method to quantify perturbation effects at single-cell resolution using manifold-based likelihood estimation and vertex frequency clustering, achieving higher accuracy than existing methods.

The paper introduces Multiscale PHATE, a method for exploring high-dimensional biomedical data, and applies it to identify immune cell signatures associated with severe COVID-19 outcomes.