Package: MSiP 1.3.7

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.

Authors:Matineh Rahmatbakhsh [aut, cre]

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Peer review:

Bug tracker:https://github.com/mrbakhsh/msip/issues

On CRAN:

ap-msbait-prey-interactions

8 exports 0.63 score 102 dependencies 3 scripts 204 downloads

Last updated 3 years agofrom:17fea87732. Checks:OK: 1 NOTE: 6. Indexed: yes.

Exports:cPASSdiceCoefficientjaccardCoefficientoverlapScorerfTrainsimpsonCoefficientsvmTrainWeighted.matrixModel

Dependencies:backportsbitbit64bootbroomcaretclassclicliprclockcodetoolscolorspacecpp11crayondata.tablediagramdigestdplyre1071fansifarverforcatsforeachfuturefuture.applygenericsggplot2glmnetglobalsgluegowergtablehardhathavenhmsipredisobanditeratorsjomoKernSmoothlabelinglatticelavalifecyclelistenvlme4lubridatemagrittrMASSMatrixmgcvmiceminqamitmlModelMetricsmunsellnlmenloptrnnetnumDerivordinalpanparallellypillarpkgconfigplyrprettyunitspROCprodlimprogressprogressrproxyPRROCpurrrR6rangerRColorBrewerRcppRcppEigenreadrrecipesreshape2rlangrpartscalesshapeSQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetzdbucminfutf8vctrsviridisLitevroomwithr