Kane Toh

The paper develops VID, an ensemble machine learning model to predict virus-infected cells in scRNA-seq data, revealing biological differences between infected and uninfected cells in virus-induced cancers.

This paper introduces a framework to infer gene regulatory networks (GRNs) during tissue morphogenesis by integrating cell movement and gene expression data.

The paper shows that zebrafish neuromesodermal progenitors undergo a critical state transition in vivo, marked by increased gene expression variability and heterogeneity before differentiation.