Yifeng Tao

The paper introduces CITRUS, a deep learning model that infers transcriptional programs influenced by somatic alterations across 17 cancer types using genomic, transcriptomic, and epigenomic data from The Cancer Genome Atlas.

This paper introduces DeepGRMF, a deep learning model that predicts cell-drug sensitivities by integrating drug structures, signaling impacts, and cell states, outperforming existing methods on cancer datasets.

The paper develops computational methods combining sequencing and miFISH data to improve accurate inference of clonal copy number evolution in tumors.

This paper introduces a system using deep reinforcement learning to automatically curate a genetic association database by simulating human research behaviors.