Network


Latest external collaboration on country level. Dive into details by clicking on the dots.

Hotspot


Dive into the research topics where Youping Deng is active.

Publication


Featured researches published by Youping Deng.


Genome Biology | 2015

Comparison of RNA-seq and microarray-based models for clinical endpoint prediction

Wenqian Zhang; Falk Hertwig; Jean Thierry-Mieg; Wenwei Zhang; Danielle Thierry-Mieg; Jian Wang; Cesare Furlanello; Viswanath Devanarayan; Jie Cheng; Youping Deng; Barbara Hero; Huixiao Hong; Meiwen Jia; Li Li; Simon Lin; Yuri Nikolsky; André Oberthuer; Tao Qing; Zhenqiang Su; Ruth Volland; Charles Wang; May D. Wang; Junmei Ai; Davide Albanese; Shahab Asgharzadeh; Smadar Avigad; Wenjun Bao; Marina Bessarabova; Murray H. Brilliant; Benedikt Brors

BackgroundGene expression profiling is being widely applied in cancer research to identify biomarkers for clinical endpoint prediction. Since RNA-seq provides a powerful tool for transcriptome-based applications beyond the limitations of microarrays, we sought to systematically evaluate the performance of RNA-seq-based and microarray-based classifiers in this MAQC-III/SEQC study for clinical endpoint prediction using neuroblastoma as a model.ResultsWe generate gene expression profiles from 498 primary neuroblastomas using both RNA-seq and 44 k microarrays. Characterization of the neuroblastoma transcriptome by RNA-seq reveals that more than 48,000 genes and 200,000 transcripts are being expressed in this malignancy. We also find that RNA-seq provides much more detailed information on specific transcript expression patterns in clinico-genetic neuroblastoma subgroups than microarrays. To systematically compare the power of RNA-seq and microarray-based models in predicting clinical endpoints, we divide the cohort randomly into training and validation sets and develop 360 predictive models on six clinical endpoints of varying predictability. Evaluation of factors potentially affecting model performances reveals that prediction accuracies are most strongly influenced by the nature of the clinical endpoint, whereas technological platforms (RNA-seq vs. microarrays), RNA-seq data analysis pipelines, and feature levels (gene vs. transcript vs. exon-junction level) do not significantly affect performances of the models.ConclusionsWe demonstrate that RNA-seq outperforms microarrays in determining the transcriptomic characteristics of cancer, while RNA-seq and microarray-based models perform similarly in clinical endpoint prediction. Our findings may be valuable to guide future studies on the development of gene expression-based predictive models and their implementation in clinical practice.


BMC Medical Genomics | 2013

Integrated lipidomics and transcriptomic analysis of peripheral blood reveals significantly enriched pathways in type 2 diabetes mellitus

Chen Zhao; Jinghe Mao; Junmei Ai; Ming Shenwu; Tieliu Shi; Daqing Zhang; Xiaonan Wang; Yunliang Wang; Youping Deng

BackgroundInsulin resistance is a key element in the pathogenesis of type 2 diabetes mellitus. Plasma free fatty acids were assumed to mediate the insulin resistance, while the relationship between lipid and glucose disposal remains to be demonstrated across liver, skeletal muscle and blood.MethodsWe profiled both lipidomics and gene expression of 144 total peripheral blood samples, 84 from patients with T2D and 60 from healthy controls. Then, factor and partial least squares models were used to perform a combined analysis of lipidomics and gene expression profiles to uncover the bioprocesses that are associated with lipidomic profiles in type 2 diabetes.ResultsAccording to factor analysis of the lipidomic profile, several species of lipids were found to be correlated with different phenotypes, including diabetes-related C23:2CE, C23:3CE, C23:4CE, ePE36:4, ePE36:5, ePE36:6; race-related (African-American) PI36:1; and sex-related PE34:1 and LPC18:2. The major variance of gene expression profile was not caused by known factors and no significant difference can be directly derived from differential gene expression profile. However, the combination of lipidomic and gene expression analyses allows us to reveal the correlation between the altered lipid profile with significantly enriched pathways, such as one carbon pool by folate, arachidonic acid metabolism, insulin signaling pathway, amino sugar and nucleotide sugar metabolism, propanoate metabolism, and starch and sucrose metabolism. The genes in these pathways showed a good capability to classify diabetes samples.ConclusionCombined analysis of gene expression and lipidomic profiling reveals type 2 diabetes-associated lipid species and enriched biological pathways in peripheral blood, while gene expression profile does not show direct correlation. Our findings provide a new clue to better understand the mechanism of disordered lipid metabolism in association with type 2 diabetes.


Pharmacogenomics Journal | 2010

A comparison of batch effect removal methods for enhancement of prediction performance using MAQC-II microarray gene expression data

Jun Luo; Martin Schumacher; Andreas Scherer; Despina Sanoudou; Dalila B. Megherbi; Timothy S. Davison; Tieliu Shi; Weida Tong; Leming Shi; Huixiao Hong; C Zhao; Fathi Elloumi; Weiwei Shi; Russell S. Thomas; Simon Lin; G. Tillinghast; G. Liu; Yiming Zhou; Damir Herman; Y Li; Youping Deng; Hong Fang; Pierre R. Bushel; M. Woods; J. Zhang


Nature Biotechnology | 2010

Consistency of predictive signature genes and classifiers generated using different microarray platforms

Xiaohui Fan; Edward K. Lobenhofer; Minjun Chen; Weiwei Shi; J. Huang; Jun Luo; J. Zhang; Stephen J. Walker; Tzu-Ming Chu; Li Li; Russell D. Wolfinger; Wenjun Bao; Richard S. Paules; Pierre R. Bushel; Jianying Li; Tieliu Shi; Tatiana Nikolskaya; Yuri Nikolsky; Huixiao Hong; Youping Deng; Y. Cheng; Hong Fang; Leming Shi; Weida Tong


Nature Biotechnology | 2010

Functional analysis of multiple genomic signatures demonstrates that classification algorithms choose phenotype-related genes

Weiwei Shi; Marina Bessarabova; D. Dosymbekov; Z. Dezso; Tatiana Nikolskaya; M. Dudoladova; T. Serebryiskaya; A. Bugrim; A. Guryanov; R.J. Brennan; R. Shah; Joaquín Dopazo; Minjun Chen; Youping Deng; Tieliu Shi; Giuseppe Jurman; Cesare Furlanello; Russell S. Thomas; J.C. Corton; Weida Tong; Leming Shi; Yuri Nikolsky


BIOCOMP | 2009

Plasma Lipidomic Profiling Reveals the Potential Biomarkers in Type 2 Diabetic Patients.

Jinghe Mao; Ming Shenwu; Marketta Blue; Manuel Ong; Mary R. Roth; Ruth Welti; Youping Deng

Collaboration


Dive into the Youping Deng's collaboration.

Top Co-Authors

Avatar

Tieliu Shi

East China Normal University

View shared research outputs
Top Co-Authors

Avatar

Huixiao Hong

Food and Drug Administration

View shared research outputs
Top Co-Authors

Avatar

Leming Shi

National Center for Toxicological Research

View shared research outputs
Top Co-Authors

Avatar

Weida Tong

Food and Drug Administration

View shared research outputs
Top Co-Authors

Avatar
Top Co-Authors

Avatar
Top Co-Authors

Avatar

Hong Fang

Food and Drug Administration

View shared research outputs
Top Co-Authors

Avatar
Top Co-Authors

Avatar

Jun Luo

Johns Hopkins University School of Medicine

View shared research outputs
Top Co-Authors

Avatar

Junmei Ai

Rush University Medical Center

View shared research outputs
Researchain Logo
Decentralizing Knowledge