Antoaneta Vladimirova

This paper develops a distributed pipeline for immuno-oncology biomarker discovery using federated learning to integrate clinical and molecular data across institutions without sharing sensitive data.

This paper introduces SEQUOIA, a transformer model that predicts cancer transcriptomes from histology images, achieving high accuracy in predicting gene expression and recurrence risk in breast cancer.