In the field of pharmacogenomics and precision medicine, gene expression analysis has
become a crucial tool in predicting patient drug response. My contributions to this field come …

Drug screening data from massive bulk gene expression databases can be analyzed to
determine the optimal clinical application of cancer drugs. The growing amount of single-cell …

Precision medicine entails the design of therapies that are matched for each individual
patient. Thus, predictive modeling of anti-cancer drug responses for specific patients …

High-throughput screening platforms for the profiling of drug sensitivity of hundreds of
cancer cell lines (CCLs) have generated large datasets that hold the potential to unlock …

A major challenge in cancer treatment is predicting the clinical response to anti-cancer
drugs on a personalized basis. The success of such a task largely depends on the ability to …

Drug response prediction arises from both basic and clinical research of personalized
therapy, as well as drug discovery for cancers. With gene expression profiles and other …

Inter and intra-tumoral heterogeneity are major stumbling blocks in the treatment of cancer
and are responsible for imparting differential drug responses in cancer patients. Recently …

The computational prediction of drug responses based on the analysis of multiple types of
genome-wide molecular data is vital for accomplishing the promise of precision medicine in …

The single‐cell RNA sequencing (scRNA‐seq) quantifies the gene expression of individual
cells, while the bulk RNA sequencing (bulk RNA‐seq) characterizes the mixed transcriptome …

Resistance to therapy remains a major cause of cancer treatment failures, resulting in many
cancer-related deaths. Resistance can occur at any time during the treatment, even at the …