Yidong Chen, Ph.D.



Greehey Children's Cancer Research Institute

Chen, Yidong, Ph.D.


Personal Statement:

Dr. Yidong Chen received his B.S./M.S. degrees in Electrical Engineering from Fudan University, Shanghai, China, and Ph.D. in Imaging Science from Rochester Institute of Technology, Rochester, NY. He has been with Hewlett Packard Co as a Research Engineer before he joined National Institutes of Health (NIH) at 1996.

At NIH, Dr. Chen joined microarray technology development effort at National Human Genome Research Institute (NHGRI), as a Special Expert, Staff Scientist, and later Associate Investigator for microarray image, statistical analysis, and bioinformatics. From 2006-2008 he joined the Genetics branch at National Cancer Institute (NCI) as a staff scientist. During the 13-year period with NHGRI and NCI, he has contributed about 90 peer reviewed publications and book chapters.

His lab works on computational biology and bioinformatics and focuses on developing computational solution and statistical modeling to bridge between quatitative science and the basic biology and translational research within Greehey Children’s Cancer Research Institute and around UT Health Science Center. Our research contributions are in:

– Support Genome Sequencing Facility (GSF) bioinformatics operation

– Develop Next-Generation Sequencing (NGS) data analysis methods

– Cancer genome profiling, gene expression analysis, Gene regulation networks, and

– Provide computational biology and biostatistics collaboration for pediatric cancer research


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Computational Biology and Bioinformatics program is the home for High-Performance Computing (HPC) infrastructure, large data storage, bioinformatics expertise, and interdisciplinary research experience to manage and distribute complex genomic data sets, to develop and perform bioinformatics tasks, machine learning techniques, and systems biology using genomic data.

We aim to establish as world-leading bioinformatics group of developing new biomedical technologies and computational methods to the varieties of high-throughput and multi-omics datasets. Our research area includes Systems biology, pan-cancer bioinformatics; Deep Learning methods in genomics, novel bioinformatics algorithm for NGS applications, and bioinformatics tools in proteo-genomics data analysis. By working with faculty from the Biostatistics Division, we will provide a wide range of research collaboration and education partners.