The 'MSiP' is a computational approach to predict proteinprotein interactions from largescale affinity purification mass 'spectrometry' (APMS) data. This approach includes both spoke and matrix models for interpreting APMS data in a network context. The "spoke" model considers only baitprey interactions, whereas the "matrix" model assumes that each of the identified proteins (baits and prey) in a given APMS experiment interacts with each of the others. The spoke model has a high falsenegative rate, whereas the matrix model has a high falsepositive rate. Although, both statistical models have merits, a combination of both models has shown to increase the performance of machine learning classifiers in terms of their capabilities in discrimination between true and false positive interactions.
Package details 


Author  Matineh Rahmatbakhsh [aut, cre] 
Maintainer  Matineh Rahmatbakhsh <matinerb.94@gmail.com> 
License  GPL3 
Version  1.3.7 
Package repository  View on CRAN 
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