'MassSpectrometry' Interaction Prediction

The 'MSiP' is a computational approach to predict protein-protein interactions from large-scale affinity purification mass 'spectrometry' (AP-MS) data. This approach includes both spoke and matrix models for interpreting AP-MS data in a network context. The "spoke" model considers only bait-prey interactions, whereas the "matrix" model assumes that each of the identified proteins (baits and prey) in a given AP-MS experiment interacts with each of the others. The spoke model has a high false-negative rate, whereas the matrix model has a high false-positive 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.


Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.


1.3.7 by Matineh Rahmatbakhsh, 4 months ago

Browse source code at https://github.com/cran/MSiP

Authors: Matineh Rahmatbakhsh [aut, cre]

Documentation:   PDF Manual  

GPL-3 license

Imports dplyr, tibble, tidyr, magrittr, plyr, PRROC, caret, e1071, mice, pROC, ranger

Suggests knitr, markdown

See at CRAN