Jinru Shia

This paper identifies a five-protein stem cell signature using a deep neural network that predicts relapse in stage III colorectal cancer patients.

The paper develops Mosaic, a machine learning system that classifies tumors into detailed subtypes and identifies molecular phenotypes using histopathologic and genomic data from a large patient cohort.

This study identifies genetic and non-genetic resistance mechanisms to KRAS inhibition in CRC using single-cell spatial transcriptomics and exome sequencing, revealing inflammatory and fetal-like transcriptional signatures associated with resistance.

The paper develops patient-derived organoids and mouse models of appendiceal cancer to identify phenotypic changes and drug targets in peritoneal metastasis.

MiMSI improves MSI detection in low tumor purity samples using deep learning and multiple instance learning, achieving higher sensitivity and auROC than existing methods.