C
Cheng Li
Researcher at Peking University
Publications - 238
Citations - 49764
Cheng Li is an academic researcher from Peking University. The author has contributed to research in topics: Gene & Medicine. The author has an hindex of 63, co-authored 200 publications receiving 42539 citations. Previous affiliations of Cheng Li include University of California, Los Angeles & Tongji University.
Papers
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Journal ArticleDOI
Subclinical hypothyroidism and its association with lupus nephritis: a case control study in a large cohort of Chinese systemic lupus erythematosus patients
TL;DR: The results of the present study suggest that SCH is a common complication in SLE patients, and closely related with lupus nephritis, and close related with LN.
Journal ArticleDOI
Computational inference of mRNA stability from histone modification and transcriptome profiles
Chengyang Wang,Rui Tian,Qian Zhao,Han Xu,Clifford A. Meyer,Cheng Li,Yong Zhang,X. Shirley Liu +7 more
TL;DR: This is the first computational model of mRNA stability inference that compares transcriptome and epigenome profiles, and provides an alternative strategy for directing experimental measurements, that concludes that the systematical use of histone modifications can differentiate non-expressed mRNAs from unstable mRNas, and distinguish stable m RNAs from highly expressed ones.
Journal ArticleDOI
Transcription factor-pathway coexpression analysis reveals cooperation between SP1 and ESR1 on dysregulating cell cycle arrest in non-hyperdiploid multiple myeloma
Xujun Wang,Zhenyu Yan,Mariateresa Fulciniti,Yingxiang Li,Maria Gkotzamanidou,Samir B. Amin,Parantu K. Shah,Yong Zhang,Nikhil C. Munshi,Cheng Li +9 more
TL;DR: A TrF-pathway coexpression analysis is developed to identify altered coexpression between two sample types of myeloma and motivates a cooperation model of ESR1 and SP1 in regulating cell cycle arrest, and a hypothesis that their overactivation in NHMM disrupts proper regulation ofcell cycle arrest.
Book ChapterDOI
A Survey of Classification Techniques for Microarray Data Analysis
TL;DR: This chapter is a survey of common classification techniques and related methods to increase their accuracies for microarray analysis based on data mining methodology.
Journal ArticleDOI
The discovery of putative urine markers for the specific detection of prostate tumor by integrative mining of public genomic profiles.
TL;DR: A simple and efficient strategy to derive candidate urine markers for prostate tumor by mining cancer genomic profiles from public databases and suggested a few urine markers as preferred prognostic markers to monitor the invasion and progression of PCa.