Ruonan Li

TmSNet predicts tumor cell-specific total mRNA expression from multi-omic data, achieving high accuracy across cancer types and external cohorts.

The paper develops DeMixNB, a deconvolution model for sparse-count RNA sequencing data, showing its utility in analyzing tumor cell plasticity in breast and lung cancer.

The paper develops CliPP to estimate subclonal mutation load (sML) and shows it predicts survival and immunotherapy response in cancers with low to moderate TMB.

This study shows that Dscam overexpression in Down syndrome mouse models causes excessive GABAergic inhibition of neocortical pyramidal neurons.

CliPP reconstructs subclonal architecture in cancer DNA sequencing data efficiently and accurately, outperforming existing methods in speed while maintaining high concordance with consensus calls.

The paper introduces isoTarget, a genetic method to block specific splicing isoforms and tag them for analysis, revealing their distinct roles in neuronal development.