Dr. Chao Cheng is a computational biologist with research focusing on cancer systems biology. His group has developed and applied computational approaches that integrate different sources of biomedical data to address clinically relevant questions, with the goal to improve cancer prevention, diagnosis and treatment.
Dr. Cheng obtained his BSc from East China University of Science and Technology in 1999, with a primary major in Biochemistry and a secondary major in Applied Mathematics. He received his MS in Molecular Genetics in 2002 from Fudan University, China. He then moved to the USA and obtained his MS in Statistics and Ph.D in Computational Biology and Bioinformatics from the University of Southern California in 2007.
From 2008-2012 Dr. Cheng worked as a post-doctorial/research scientist in the computational biology group led by Prof. Mark Gerstein at Yale University. During this time, he actively participated in the international consortium projects, ENCODE (the Encyclopedia of DNA Element) and modENCODE (ENCODE in model organisms), which aimed to delineate the organization and regulation of the genome of human and model organisms (fly and worm). Dr. Cheng started his independent research group at the Geisel School of Medicine at Dartmouth College and the Norris Cotton Cancer Center in 2012.
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Dr. Chao Cheng is a computational biologist with research focusing on cancer systems biology. His group has developed and applied computational approaches that integrate different sources of biomedical data to address clinically relevant questions, with the goal to improve cancer prevention, diagnosis and treatment.
Dr. Cheng obtained his BSc from East China University of Science and Technology in 1999, with a primary major in Biochemistry and a secondary major in Applied Mathematics. He received his MS in Molecular Genetics in 2002 from Fudan University, China. He then moved to the USA and obtained his MS in Statistics and Ph.D in Computational Biology and Bioinformatics from the University of Southern California in 2007.
From 2008-2012 Dr. Cheng worked as a post-doctorial/research scientist in the computational biology group led by Prof. Mark Gerstein at Yale University. During this time, he actively participated in the international consortium projects, ENCODE (the Encyclopedia of DNA Element) and modENCODE (ENCODE in model organisms), which aimed to delineate the organization and regulation of the genome of human and model organisms (fly and worm). Dr. Cheng started his independent research group at the Geisel School of Medicine at Dartmouth College and the Norris Cotton Cancer Center in 2012.
At Dartmouth, his group focused on cancer systems biology with an emphasis on translational cancer research. He has taken advantage of his expertise in human genomic studies and developed computational methods to integrate cancer data with other genomic data sources. By developing and applying systems-based approaches, he has contributed to three areas of cancer research -- transcriptional regulation, cancer immunology and oncological drug repurposing.
Dr. Cheng’s group has developed and applied computational methods to 1) predict new candidate drugs for the treatment of cancer, and validated some of the predicted drugs using experimental and/or epidemiological analysis; 2) infer the activity of important regulatory proteins and microRNAs in cancer; 3) develop gene signature-based biomarkers to predict cancer prognosis and patient sensitivity to specific therapeutic treatments such as neoadjuvant therapy; and 4) investigate immune cell infiltration and perform systematic analyses that reveal the complex functions of different immune cell types during cancer development, progression, metastasis, and therapeutic treatment.
Dr. Cheng has published more than 130 peer-reviewed articles and 4 book chapters, and has a total of over 15,000 citations. Of of these publications, more than 80 have been published after 2012 when he established his independent research group at the Geisel School of Medicine at Dartmouth. Most of these publications are directly related to translational cancer research, including new prognostic and predictive biomarkers as well as new candidate cancer drugs.
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