Estimating node degree in bait-prey graphs

Denise Scholtens*, Tony Chiang, Wolfgang Huber, Robert Gentleman

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

Motivation: Proteins work together to drive biological processes in cellular machines. Summarizing global and local properties of the set of protein interactions, the interactome, is necessary for describing cellular systems. We consider a relatively simple per-protein feature of the interactome: The number of interaction partners for a protein, which in graph terminology is the degree of the protein. Results: Using data subject to both stochastic and systematic sources of false positive and false negative observations, we develop an explicit probability model and resultant likelihood method to estimate node degree on portions of the interactome assayed by bait-prey technologies. This approach yields substantial improvement in degree estimation over the current practice that naïvely sums observed edges. Accurate modeling of observed data in relation to true but unknown parameters of interest gives a formal point of reference from which to draw conclusions about the system under study.

Original languageEnglish (US)
Pages (from-to)218-224
Number of pages7
JournalBioinformatics
Volume24
Issue number2
DOIs
StatePublished - Jan 15 2008

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

Fingerprint Dive into the research topics of 'Estimating node degree in bait-prey graphs'. Together they form a unique fingerprint.

Cite this