Protein network inference from multiple genomic data: a supervised approach

@article{Yamanishi2004ProteinNI,
  title={Protein network inference from multiple genomic data: a supervised approach},
  author={Yoshihiro Yamanishi and Jean-Philippe Vert and Minoru Kanehisa},
  journal={Bioinformatics},
  year={2004},
  volume={20 Suppl 1},
  pages={
          i363-70
        }
}
MOTIVATION An increasing number of observations support the hypothesis that most biological functions involve the interactions between many proteins, and that the complexity of living systems arises as a result of such interactions. In this context, the problem of inferring a global protein network for a given organism, using all available genomic data about the organism, is quickly becoming one of the main challenges in current computational biology. RESULTS This paper presents a new method… CONTINUE READING

Citations

Publications citing this paper.
SHOWING 1-10 OF 174 CITATIONS

Determining Nucleolar Association from Sequence by Leveraging Protein-Protein Interactions

  • Journal of Computational Biology
  • 2008
VIEW 4 EXCERPTS
CITES METHODS & BACKGROUND
HIGHLY INFLUENCED

Inferring cellular networks – a review

  • BMC Bioinformatics
  • 2007
VIEW 4 EXCERPTS
CITES METHODS & BACKGROUND
HIGHLY INFLUENCED

Kernel matrix regression

VIEW 5 EXCERPTS
CITES METHODS & BACKGROUND

Supervised inference of gene-regulatory networks

  • BMC Bioinformatics
  • 2007
VIEW 5 EXCERPTS
CITES BACKGROUND & METHODS
HIGHLY INFLUENCED

FILTER CITATIONS BY YEAR

2003
2019

CITATION STATISTICS

  • 12 Highly Influenced Citations

  • Averaged 7 Citations per year from 2017 through 2019

References

Publications referenced by this paper.
SHOWING 1-10 OF 24 REFERENCES