Matthew D. Montierth

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.

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 quantifies tumor-specific total mRNA expression from bulk data and shows its association with clinical outcomes across 15 cancer types.

The paper introduces Famdenovo, a model to predict de novo mutations in families with Li-Fraumeni syndrome, identifying 40 new carriers and showing high prediction accuracy.