Dr. Tan studies transcriptional regulation during normal development and disease. This involves the interplay of multiple transcription and epigenetic factors in a 3D chromosomal environment. Using experimental genomics and computational modeling, Dr. Tan investigates transcriptional regulatory networks underlying embryonic hematopoiesis, T cell differentiation, and pediatric leukemia.
Dr. Diskin's research is focused on translational genomics in childhood cancers. Her laboratory seeks to identify the genetic basis of childhood cancers by combining quantitative computational methods with rigorous "wet-lab" experimental approaches. In parallel, she has developed, and is applying, a proteogenomic approach to identify novel immunotherapeutic targets for high-risk and relapsed pediatric malignancies.
Dr. Jolley's research lab is focused on visualizing and quantifying congenitally abnormal heart structures using 3D imaging with the driving goal of informing surgical and interventional planning in children and adults with congenital heart disease.
Dr. Wang's research focuses on the development of bioinformatics methods to improve the understanding of the genetic basis of human diseases, and the integration of electronic health records and genomic information to facilitate genomic medicine on scale.
Dr. Zhou’s outstanding research interests include mitosis-coupled DNA methylation drift and inference of cell-type-specific epigenetic signals. He developed multiple computational tools for analyzing DNA methylation data and has actively contributed to cancer genomics data analysis.
Dr. Zorc's work focuses on the intersection of interventional clinical research, quality improvement (QI), and clinical informatics. He has formal certification in epidemiology, QI methodology, and clinical informatics, and has participated in multi-center research networks, guideline and improvement collaboratives, and electronic health record development locally and nationally.
The Arcus team at the Research Institute is solving a number of challenges at once: Decrease the time it takes for researchers to access data, increase the reproducibility of research, ensure data security, and speed up the rate of breakthroughs.