List M, Amirabad AD, Kostka D, Schulz MH (2019). “Large-scale inference of competing endogenous RNA networks with sparse partial correlation.” Bioinformatics. doi:10.1093/bioinformatics/btz314. https://academic.oup.com/bioinformatics/article-pdf/35/14/i596/28913523/btz314.pdf, https://doi.org/10.1093/bioinformatics/btz314.

Corresponding BibTeX entry:

  @Article{,
    author = {Markus List and Azim Dehghani Amirabad and Dennis Kostka
      and Marcel H. Schulz},
    title = {Large-scale inference of competing endogenous RNA networks
      with sparse partial correlation},
    year = {2019},
    doi = {10.1093/bioinformatics/btz314},
    abstract = {MicroRNAs (miRNAs) are important non-coding
      post-transcriptional regulators that are involved in many
      biological processes and human diseases. Individual miRNAs may
      regulate hundreds of genes, giving rise to a complex gene
      regulatory network in which transcripts carrying miRNA binding
      sites act as competing endogenous RNAs (ceRNAs). Several methods
      for the analysis of ceRNA interactions exist, but these do often
      not adjust for statistical confounders or address the problem
      that more than one miRNA interacts with a target transcript. We
      present SPONGE, a method for the fast construction of ceRNA
      networks. SPONGE uses ?multiple sensitivity correlation?, a newly
      defined measure for which we can estimate a distribution under a
      null hypothesis. SPONGE can accurately quantify the contribution
      of multiple miRNAs to a ceRNA interaction with a probabilistic
      model that addresses previously neglected confounding factors and
      allows fast P-value calculation, thus outperforming existing
      approaches. We applied SPONGE to paired miRNA and gene expression
      data from The Cancer Genome Atlas for studying global effects of
      miRNA-mediated cross-talk. Our results highlight already
      established and novel protein-coding and non-coding ceRNAs which
      could serve as biomarkers in cancer.},
    url = {https://doi.org/10.1093/bioinformatics/btz314},
    eprint =
      {https://academic.oup.com/bioinformatics/article-pdf/35/14/i596/28913523/btz314.pdf},
    journal = {Bioinformatics},
  }