Chao Cheng

Summary

Affiliation: Yale University
Country: USA

Publications

  1. ncbi Understanding protein evolutionary rate by integrating gene co-expression with protein interactions
    Kaifang Pang
    Department of Computer Science and Engineering, Shanghai Jiao Tong University, China
    BMC Syst Biol 4:179. 2010
  2. ncbi Systematic identification of transcription factors associated with patient survival in cancers
    Chao Cheng
    Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
    BMC Genomics 10:225. 2009
  3. ncbi mRNA expression profiles show differential regulatory effects of microRNAs between estrogen receptor-positive and estrogen receptor-negative breast cancer
    Chao Cheng
    Program in Computational Biology and Bioinformatics, Yale University, George Street, New Haven, CT 06511, USA
    Genome Biol 10:R90. 2009
  4. ncbi The relationship between the evolution of microRNA targets and the length of their UTRs
    Chao Cheng
    Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
    BMC Genomics 10:431. 2009
  5. ncbi Construction and analysis of an integrated regulatory network derived from high-throughput sequencing data
    Chao Cheng
    Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut, USA
    PLoS Comput Biol 7:e1002190. 2011
  6. ncbi A statistical framework for modeling gene expression using chromatin features and application to modENCODE datasets
    Chao Cheng
    Department of Molecular Biophysics and Biochemistry, Yale University, 260 Whitney Avenue, New Haven, CT 06520, USA
    Genome Biol 12:R15. 2011
  7. ncbi Prediction and characterization of noncoding RNAs in C. elegans by integrating conservation, secondary structure, and high-throughput sequencing and array data
    Zhi John Lu
    Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut 06520, USA
    Genome Res 21:276-85. 2011
  8. ncbi Modeling the relative relationship of transcription factor binding and histone modifications to gene expression levels in mouse embryonic stem cells
    Chao Cheng
    Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut 06520, USA
    Nucleic Acids Res 40:553-68. 2012
  9. ncbi TIP: a probabilistic method for identifying transcription factor target genes from ChIP-seq binding profiles
    Chao Cheng
    Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT 06511, USA
    Bioinformatics 27:3221-7. 2011
  10. ncbi Integrative analysis of the Caenorhabditis elegans genome by the modENCODE project
    Mark B Gerstein
    Program in Computational Biology and Bioinformatics, Yale University, Bass 432, 266 Whitney Avenue, New Haven, CT 06520, USA
    Science 330:1775-87. 2010

Detail Information

Publications11

  1. ncbi Understanding protein evolutionary rate by integrating gene co-expression with protein interactions
    Kaifang Pang
    Department of Computer Science and Engineering, Shanghai Jiao Tong University, China
    BMC Syst Biol 4:179. 2010
    ..Among the many factors determining protein evolutionary rate, protein-protein interaction degree (PPID) has been intensively investigated in recent years, but its precise effect on protein evolutionary rate is still heavily debated...
  2. ncbi Systematic identification of transcription factors associated with patient survival in cancers
    Chao Cheng
    Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
    BMC Genomics 10:225. 2009
    ..However, the association between transcription factors and cancers is largely dependent on the transcription regulatory activities rather than mRNA expression levels...
  3. ncbi mRNA expression profiles show differential regulatory effects of microRNAs between estrogen receptor-positive and estrogen receptor-negative breast cancer
    Chao Cheng
    Program in Computational Biology and Bioinformatics, Yale University, George Street, New Haven, CT 06511, USA
    Genome Biol 10:R90. 2009
    ..Given this, it is useful to define an overall metric of regulatory effect for a specific microRNA and see how this changes across different conditions...
  4. ncbi The relationship between the evolution of microRNA targets and the length of their UTRs
    Chao Cheng
    Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
    BMC Genomics 10:431. 2009
    ..Their widespread and important role in animals is gauged by estimates that approximately 25% of all genes are miRNA targets...
  5. ncbi Construction and analysis of an integrated regulatory network derived from high-throughput sequencing data
    Chao Cheng
    Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut, USA
    PLoS Comput Biol 7:e1002190. 2011
    ..As more and more genome-wide ChIP-Seq and RNA-Seq data becomes available in the near future, our methods of data integration have various potential applications...
  6. ncbi A statistical framework for modeling gene expression using chromatin features and application to modENCODE datasets
    Chao Cheng
    Department of Molecular Biophysics and Biochemistry, Yale University, 260 Whitney Avenue, New Haven, CT 06520, USA
    Genome Biol 12:R15. 2011
    ..Moreover, our framework reveals the positional contribution around genes (upstream or downstream) of distinct chromatin features to the overall prediction of expression levels...
  7. ncbi Prediction and characterization of noncoding RNAs in C. elegans by integrating conservation, secondary structure, and high-throughput sequencing and array data
    Zhi John Lu
    Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut 06520, USA
    Genome Res 21:276-85. 2011
    ..Overall, our study identifies many new potential ncRNAs in C. elegans and provides a method that can be adapted to other organisms...
  8. ncbi Modeling the relative relationship of transcription factor binding and histone modifications to gene expression levels in mouse embryonic stem cells
    Chao Cheng
    Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut 06520, USA
    Nucleic Acids Res 40:553-68. 2012
    ..Finally, we found that the models trained solely on protein-coding genes are predictive of expression levels of microRNAs, suggesting that their regulation by TFs and HMs may share a similar mechanism to that for protein-coding genes...
  9. ncbi TIP: a probabilistic method for identifying transcription factor target genes from ChIP-seq binding profiles
    Chao Cheng
    Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT 06511, USA
    Bioinformatics 27:3221-7. 2011
    ..However, this does not take into account the number of sites upstream of the TSS, their exact positioning or the fact that different TFs appear to act at different characteristic distances from the TSS...
  10. ncbi Integrative analysis of the Caenorhabditis elegans genome by the modENCODE project
    Mark B Gerstein
    Program in Computational Biology and Bioinformatics, Yale University, Bass 432, 266 Whitney Avenue, New Haven, CT 06520, USA
    Science 330:1775-87. 2010
    ..Integrating data types, we built statistical models relating chromatin, transcription factor binding, and gene expression. Overall, our analyses ascribed putative functions to most of the conserved genome...
  11. ncbi Zebrafish miR-1 and miR-133 shape muscle gene expression and regulate sarcomeric actin organization
    Yuichiro Mishima
    Department of Genetics, Yale University School of Medicine, New Haven, Connecticut 06510, USA
    Genes Dev 23:619-32. 2009
    ..These results suggest that miR-1 and miR-133 actively shape gene expression patterns in muscle tissue, where they regulate sarcomeric actin organization...