We recently welcomed several new faculty members:

Jing Chen, PhD: Combining Data to Predict Disease

Jing Chen, PhD, is a bioinformatician with more than 10 years’ experience in computational biology. He is currently researching methods to combine molecular, clinical, and phenotypic data to predict genes and pathogenic variants as they predispose individuals to particular pediatric diseases including childhood cancer, preterm birth, and genetic diseases.

Dr. Chen earned a PhD in bioinformatics from the University of Cincinnati. He did postdoctoral research at the University of Cincinnati’s Department of Environmental Health and Cincinnati Children’s Division of Biomedical Informatics. Under the mentorship of Bruce J. Aronow, PhD, and Anil Jegga, DVM, MRes, Chen created the ToppGene suite of web applications as part of his PhD thesis. As a research scientist at the University of Cincinnati, he proposed a statistical framework to connect transcription factors with diseases and drugs based on ChIP-seq and mRNA expression.

Dr. Chen is currently involved in several ongoing Cincinnati Children’s research projects, including efforts to develop an integrative annotation tool for personal genome variation using a graph-based approach; analysis of various maternal and fetal genetic effects on preterm birth and pregnancy outcomes; and integrative analysis of multi-omics data to target fibroblast activation in idiopathic pulmonary fibrosis.

Philip A. Hagedorn, MD, MBI: Optimizing Decision Support Tools to Improve Care

Philip A. Hagedorn, MD, MBI, is a pediatric clinician and researcher with interests in hospital medicine and clinical informatics. He is the medical director of the Liberty Campus Division of Hospital Medicine and an assistant professor in the Division of Biomedical Informatics. Dr. Hagedorn completed his residency at Cincinnati Children’s and earned a master’s in biomedical informatics from Oregon Health & Science University.

Dr. Hagedorn aims to improve the care and safety of children through optimization of Electronic Health Record (EHR) decision support tools. He develops data and pipelines to enable frontline quality improvement work through analytics and visualization. This includes examining opportunities for implementing clinical decision rules in the EHR and using analytics and visualization to target improvement of medication alerts.

Dr. Hagedorn’s current work involves continued evolution of analytics and visualization techniques to gain insight into operational and clinical data. Leveraging clinical data sources, he enables more nimble quality improvement work through visualization, notification and deeper insight into successes and failures. Dr. Hagedorn is also the lead developer of the biomedical informatics clinical rotation elective from Cincinnati Children’s and UC College of Medicine.

Hee Woong Lim, PhD: Understanding Cellular Processes with Multi-Omics Approaches

Hee Woong Lim, PhD, is a computational scientist with interests in regulatory genomics, pharmacogenomics, and machine learning. Dr. Lim earned a PhD from the school of computer science and engineering at Seoul National University in Korea, then switched into the field of bioinformatics and completed postdoctoral research at the University of Pennsylvania (Penn).

Dr. Lim investigates transcriptional regulation of gene expression in various contexts including, but not limited to, metabolism, development, pathogenesis, and pharmacogenomics. He specifically focuses on enhancer regulation and tries to explain underlying regulatory mechanisms. To this end, he integrates various levels of genomic, epigenomic, and transcriptomic information from high-throughput data (GRO-seq, RNA-seq, ChIP-seq, ChIP-exo, etc.) to understand detailed enhancer architectures and their distinct functions. View his publications here.

Dr. Lim will be establishing an interdisciplinary laboratory that collaborates with various disciplines beyond metabolic systems at Cincinnati Children’s in both pediatric and adult medicine, working toward developing open-source bioinformatics tools to assist in scientific discovery.