@article{PAR00006434,
  title = {{I}nteractional and functional centrality in transcriptional co-expression networks},
  author = {{P}rifti, {E}. and {Z}ucker, {J}ean-{D}aniel and {C}lement, {K}. and {H}enegar, {C}.},
  editor = {},
  language = {{ENG}},
  abstract = {{M}otivation: {T}he noisy nature of transcriptomic data hinders the biological relevance of conventional network centrality measures, often used to select gene candidates in co-expression networks. {T}herefore, new tools and methods are required to improve the prediction of mechanistically important transcriptional targets. {R}esults: {W}e propose an original network centrality measure, called annotation transcriptional centrality ({ATC}) computed by integrating gene expression profiles from microarray experiments with biological knowledge extracted from public genomic databases. {ATC} computation algorithm delimits representative functional domains in the co-expression network and then relies on this information to find key nodes that modulate propagation of functional influences within the network. {W}e demonstrate {ATC} ability to predict important genes in several experimental models and provide improved biological relevance over conventional topological network centrality measures.},
  keywords = {},
  booktitle = {},
  journal = {{B}ioinformatics},
  volume = {26},
  numero = {24},
  pages = {3083--3089},
  ISSN = {1367-4803},
  year = {2010},
  DOI = {10.1093/bioinformatics/btq591},
  URL = {https://www.documentation.ird.fr/hor/{PAR}00006434},
}