Connection

CHAO CHENG to Algorithms

This is a "connection" page, showing publications CHAO CHENG has written about Algorithms.
Connection Strength

1.026
  1. Inferring activity changes of transcription factors by binding association with sorted expression profiles. BMC Bioinformatics. 2007 Nov 16; 8:452.
    View in: PubMed
    Score: 0.134
  2. MARD: a new method to detect differential gene expression in treatment-control time courses. Bioinformatics. 2006 Nov 01; 22(21):2650-7.
    View in: PubMed
    Score: 0.123
  3. BioMethyl: an R package for biological interpretation of DNA methylation data. Bioinformatics. 2019 10 01; 35(19):3635-3641.
    View in: PubMed
    Score: 0.077
  4. Contextual Refinement of Regulatory Targets Reveals Effects on Breast Cancer Prognosis of the Regulome. PLoS Comput Biol. 2017 01; 13(1):e1005340.
    View in: PubMed
    Score: 0.063
  5. Application of pharmacologically induced transcriptomic profiles to interrogate PI3K-Akt-mTOR pathway activity associated with cancer patient prognosis. Oncotarget. 2016 Dec 20; 7(51):84142-84154.
    View in: PubMed
    Score: 0.063
  6. iTAR: a web server for identifying target genes of transcription factors using ChIP-seq or ChIP-chip data. BMC Genomics. 2016 08 12; 17(1):632.
    View in: PubMed
    Score: 0.062
  7. Integrative Genomic Analyses Yield Cell-Cycle Regulatory Programs with Prognostic Value. Mol Cancer Res. 2016 Apr; 14(4):332-43.
    View in: PubMed
    Score: 0.059
  8. An approach for determining and measuring network hierarchy applied to comparing the phosphorylome and the regulome. Genome Biol. 2015 Mar 31; 16:63.
    View in: PubMed
    Score: 0.056
  9. Integrative analysis of survival-associated gene sets in breast cancer. BMC Med Genomics. 2015 Mar 12; 8:11.
    View in: PubMed
    Score: 0.056
  10. DPRP: a database of phenotype-specific regulatory programs derived from transcription factor binding data. Nucleic Acids Res. 2014 Jan; 42(Database issue):D178-83.
    View in: PubMed
    Score: 0.051
  11. REACTIN: regulatory activity inference of transcription factors underlying human diseases with application to breast cancer. BMC Genomics. 2013 Jul 26; 14:504.
    View in: PubMed
    Score: 0.050
  12. Genome-wide analysis of chromatin features identifies histone modification sensitive and insensitive yeast transcription factors. Genome Biol. 2011 Nov 07; 12(11):R111.
    View in: PubMed
    Score: 0.044
  13. A statistical framework for modeling gene expression using chromatin features and application to modENCODE datasets. Genome Biol. 2011; 12(2):R15.
    View in: PubMed
    Score: 0.042
  14. mRNA expression profiles show differential regulatory effects of microRNAs between estrogen receptor-positive and estrogen receptor-negative breast cancer. Genome Biol. 2009; 10(9):R90.
    View in: PubMed
    Score: 0.038
  15. Systematic identification of cell cycle regulated transcription factors from microarray time series data. BMC Genomics. 2008 Mar 03; 9:116.
    View in: PubMed
    Score: 0.034
  16. Loregic: a method to characterize the cooperative logic of regulatory factors. PLoS Comput Biol. 2015 Apr; 11(4):e1004132.
    View in: PubMed
    Score: 0.014
  17. Unsupervised feature construction and knowledge extraction from genome-wide assays of breast cancer with denoising autoencoders. Pac Symp Biocomput. 2015; 132-43.
    View in: PubMed
    Score: 0.014
  18. OrthoClust: an orthology-based network framework for clustering data across multiple species. Genome Biol. 2014 Aug 28; 15(8):R100.
    View in: PubMed
    Score: 0.013
  19. Machine learning and genome annotation: a match meant to be? Genome Biol. 2013 May 29; 14(5):205.
    View in: PubMed
    Score: 0.012
  20. Prediction and characterization of noncoding RNAs in C. elegans by integrating conservation, secondary structure, and high-throughput sequencing and array data. Genome Res. 2011 Feb; 21(2):276-85.
    View in: PubMed
    Score: 0.010
  21. A probe-treatment-reference (PTR) model for the analysis of oligonucleotide expression microarrays. BMC Bioinformatics. 2008 Apr 14; 9:194.
    View in: PubMed
    Score: 0.009
Connection Strength

The connection strength for concepts is the sum of the scores for each matching publication.

Publication scores are based on many factors, including how long ago they were written and whether the person is a first or senior author.