Thinh Ngoc Tran

The paper uses machine learning to identify cancer driver mutations, finding that methods incorporating protein structure or functional genomic data outperform those using only evolutionary data, and validates their predictions using real-world patient cohorts.

Drosophila larval optic lobe single-cell RNA-seq and pseudotime analysis define a near-complete medulla neuroblast temporal transcription factor series, with Eyeless and Dichaete acting as regulatory hubs.

The paper shows cell cycle duration influences gene expression and cell fate by acting as a transcriptional filter, supported by simulations and single-cell RNA-seq data.

Tempora infers cell trajectories from time-series scRNA-seq data, outperforming existing methods in human and mouse developmental processes.