Vu Pham

This study demonstrates that type I-E CRISPR can selectively degrade oncogenic extrachromosomal DNA in cancer cells, reducing oncogene signaling and tumor proliferation.

pDriver identifies personalized coding and miRNA cancer drivers for individual patients using gene networks and outperforms existing methods on TCGA datasets.

DriverGroup identifies driver gene groups in cancer by analyzing gene expression and interaction data, showing improved performance over existing methods in detecting gene group regulation and providing insights into breast cancer.

This paper introduces a pseudo-time causality method to identify miRNA-mRNA interactions during biological processes, showing improved performance over existing methods in both single-cell and bulk data.

The paper predicts cellular positions in the Drosophila embryo using scRNA-seq data by benchmarking methods against spatially annotated genes.

The paper presents methods and tools for predicting cell locations in Drosophila embryos using single-cell transcriptomic data, achieving top results in two sub-challenges.

The paper introduces miRsyn, a causal inference framework to identify miRNA synergism using observational data, finding that many miRNA pairs with shared targets do not synergize at the expression level.

This paper identifies miRNA-mRNA regulatory relationships in breast cancer using invariant causal prediction and shows improved performance over existing methods.