Wenke Liu

The paper introduces a segmentation-free vision transformer, CANVAS, to discover and characterize tumor microenvironment signatures from high-dimensional multiplexed images, identifying a monocytic signature linked to poor prognosis.

This study uses a deep learning model to predict STK11 mutations in lung adenocarcinoma histopathology slides and finds that immune cell density is the key feature for classification.

This paper introduces a deep learning model that predicts endometrial cancer subtypes and mutations from histopathology images, offering a faster alternative to genomic sequencing.

The paper uses deep neural networks to predict gene perturbation from transcriptional profiles, achieving 74.9% accuracy but with limited generalizability due to data consistency issues.

This study uses independent component analysis on proteogenomic data to uncover pathway-level biological insights in breast cancer.