Assigning Confidence Scores to Protein–Protein Interactions

  • Jingkai Yu
  • Thilakam Murali
  • Russell L. FinleyJr.
Part of the Methods in Molecular Biology book series (MIMB, volume 812)


Screens for protein–protein interactions using assays like the yeast two-hybrid system have generated volumes of useful data. The protein interactions from these screens have been used to develop a better understanding of the functions of individual proteins, regulatory pathways, molecular machines, and entire biological systems. The value of this data, however, is limited by the inherent frequency of false positives that arise in most protein interaction screens. Appreciable numbers of false positives can crop up in both low-throughput and high-throughput screens, and even in screens that employ stringent criteria for defining a positive. A number of classification systems have been used to help distinguish false positives from biologically relevant true positives. This chapter describes a system for assigning a confidence score to each interaction based on the probability that it is a true positive. Such confidence scores can be used to prioritize interactions for validation. The scores are also useful for network analysis methods that take advantage of probabilistic edge weights. The scoring method does not rely on gold standard datasets of reliable true positives and true negatives, and thus circumvents the challenges associated with obtaining such datasets. Moreover, the scoring method uses data features that are largely assay-independent, making it useful for interactions obtained from a variety of different technologies and screening methods.

Key words

Interactome mapping Protein–protein interaction Protein networks Confidence scores 


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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Jingkai Yu
    • 1
  • Thilakam Murali
    • 2
  • Russell L. FinleyJr.
    • 2
  1. 1.National Key Laboratory of Biochemical EngineeringChinese Academy of SciencesBeijingChina
  2. 2.Center for Molecular Medicine and GeneticsWayne State University School of MedicineDetroitUSA

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