MSiP: '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.
||R (≥ 3.6.0)
||dplyr (≥ 1.0.6), tibble (≥ 3.1.2), tidyr (≥ 1.1.3), magrittr (≥ 2.0.1), plyr (≥ 1.8.6), PRROC (≥ 1.3.1), caret (≥ 6.0.88), e1071 (≥ 1.7.7), mice (≥ 3.13.0), pROC (≥
184.108.40.206), ranger (≥ 0.12.1)
||Matineh Rahmatbakhsh [aut, cre]
||Matineh Rahmatbakhsh <matinerb.94 at gmail.com>
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