Youping Deng, PhD

Youping Deng, PhD

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Full Member, Population Sciences of the Pacific Program, (Cancer Epidemiology), University of Hawaiʻi Cancer Center
Faculty Co-Director, Genomics and Bioinformatics Shared Resource

Academic Appointment(s):
Professor, Department of Integrative and Complementary Medicine, John A. Burns School of Medicine, University of Hawaiʻi at Mānoa
Graduate Faculty:  Molecular Biosciences and Bioengineering; Program of Biomedical Science - Clinical Research Track (BIOM), John A. Burns School of Medicine, University of Hawaiʻi at Mānoa

PhD, Molecular Pharmacology, Peking Union Medical College, Beijing, China
Post-Doctoral Fellow, Cancer and Diabetes, Wayne State University, Detroit, Michigan
Certified Bioinformatics Specialist/Master, National Bioinformatics Institute

Research Focus

The long-term goal of the Deng lab is to develop precision medicine for cancer using both bioinformatics and experimental approaches. My research is mainly centered on 4 areas:

  1. New computational method development. My lab has developed a series of innovative methods including novel algorithms for data normalization, clustering, feature selection, classification, differential expression, gene function ontology, gene network modeling and so on. We are currently developing new methods for alternative splicing and DNA somatic mutation for biomarker identification based on sequencing data.
  2. Identification of non-invasive biomarkers for early detection of cancer. We are searching for novel accurate circulating biomarkers for early detection of lung cancer and breast cancer. Based on a variety of high throughput "omics" methods including small RNA-seq, metabolomics, DNA-seq, and proteomics plus bioinformatics data mining, we have found and are seeking for circulating metabolite, ncRNA, protein, and CtDNA markers for early diagnosis of cancer.
  3. Characterization of biomarkers for predicting clinical outcomes of human diseases including cancer. We are mining public "omics" data such as TCGA data as well as generating our own high-throughput data to identify biomarkers to predict clinical outcomes of human diseases including cancer. For instance, we have found novel DNA mutation and gene expression signatures to predict better response to cancer drugs such as PARP inhibitor. New cellular and animal experiments are being designed to evaluate these biomarkers and understand their mechanisms.
  4. Integrative data analysis of "omics" and clinical data. We are integrating different types of "omics" data such as genomics, transcriptomics, metabolomics, epigenomics and proteomics data, as well as clinical factors, using a systems biology approach to understand carcinogenesis, cancer development, and find better biomarkers for precision medicine

In addition to independent research, my team also provides bioinformatics services, which primarily focuses on the analysis and management of high-throughput data such as microarray data, real-time PCR data, proteomics, metabolomics, multiple biomarker data and next-generation sequence data including DNA-seq, RNA-seq, Chip-seq and microbiota (metagenomics) data and so on. The Shared Resource also supports routine bioinformatics applications, such as phylogenetic, protein function prediction.

Selected Publications

Chen X, Chen H, Dai M, Ai J, Li Y, Mahon B, Dai S, Deng Y*. (2016). Plasma lipidomics profiling identified lipid biomarkers in distinguishing early-stage breast cancer from benign lesions. Oncotarget; May 2. doi: 10.18632/oncotarget.9124. PubMed PMID: 27153558.

Hu L, Ai J, Long H, Liu W, Wang X, Zuo Y, Li Y, Wu Q, Deng Y*. (2016). Integrative microRNA and gene profiling data analysis reveals novel biomarkers and mechanisms for lung cancer. Oncotarget; Feb 23;7(8):8441-54. doi: 10.18632/oncotarget.7264. PubMed PMID: 26870998; PubMed Central PMCID: PMC480978.

Wang C, Gong B, Buschel PR, Thierry-Mieg J, Thierry-Mieg D, Xu J, Fang H, Hong H, Shen J, Su Z, Meehan J, Li X, Yang L, Li H, Labaj PP, Kreil DP, Megherbi D, Gaj S, Caiment F, van Delft J, Kleinjans J, Scherer A, Devanarayan V, Wang J, Yang Y, Qian HR, Lancashire LJ, Bessarabova M, Nikolsky Y, Furlanello C, Chierici M, Albanese D, Jurman G, Riccadonna S, Filosi M, Visintainer R, Zhang KK, Li J, Hseih JH, Svolboda DL, Fuscoe JC, Deng Y, Shi L, Paules R Auerbach SS, Tong W. (2014). The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance. Nat Biotechnol; Sep; 32(9):926-32. doi: 10.1038/nbt.3001. Epub 2014 Aug 24. PubMed PMID: 25150839; PubMed Central PMCID: PMC4243706.

Melson J, Li Y, Cassinotti E, Melnikov A, Boni L, Ai J, Greenspan M, Mobarhan S, Levenson V, Deng Y*. (2014). Commonality and differences of methylation signatures in the plasma of patients with pancreatic cancer and colorectal cancer. Int J Cancer; Jun 1;134(11): 2656-62. doi: 10.1002/ijc.28593. Epub 2013 Nov 29. PubMed PMID: 224288256.

Pirooznia M, Habib T, Perkins EJ, Deng Y*. (2008). GOfetcher: a database with complex searching facility for gene ontolog. Bioinformatics; Nov 1;24(21):2561-3. doi:10.1093/bioinformatics/btn441. Epub 2008 Aug 26. PubMed PMID: 18728045.

Publication list via PubMed

Active Grants

Y. Deng, PI
Shanghai Realgen Biotech Inc
"Bioinformatics model development for cancer biomarker data analysis"

Y. Deng, Bioinformatics Core Director; R. Yanagihara, PI
"Pacific Center for Emerging Infectious Diseases Research"

Y. Deng, Co-I; R. Sumner, PI
"Detection and treatment of peri-implant osteolysis"