Tumor cell specific total mRNA expression informed neural networks predicts cancer progression (opens in new tab)

Inferring tumor molecular phenotypes from high-dimensional multi-omic data is a fundamental challenge in computational biology. Current methods for estimating tumor cell-specific total mRNA expression (TmS) require matched DNA and RNA sequencing data and rely on computationally intensive deconvolution pipelines. We present TmSNet, a deep learning framework that predicts TmS using mRNA, DNA methylation, miRNA, and immune cell proportions as input features. TmSNet integrates structured feature ...