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]

MSiP_1.3.7.tar.gz
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MSiP_1.3.7.tar.gz(r-4.5-noble)MSiP_1.3.7.tar.gz(r-4.4-noble)
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MSiP.pdf |MSiP.html
MSiP/json (API)

# Install 'MSiP' in R:
install.packages('MSiP', repos = c('https://mrbakhsh.r-universe.dev', 'https://cloud.r-project.org'))

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

Datasets:

On CRAN:

Conda:

ap-msbait-prey-interactions

3.70 score 3 scripts 184 downloads 8 exports 106 dependencies

Last updated 3 years agofrom:17fea87732. Checks:1 OK, 8 NOTE. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 04 2025
R-4.5-winNOTEMar 04 2025
R-4.5-macNOTEMar 04 2025
R-4.5-linuxNOTEMar 04 2025
R-4.4-winNOTEMar 04 2025
R-4.4-macNOTEMar 04 2025
R-4.4-linuxNOTEMar 04 2025
R-4.3-winNOTEMar 04 2025
R-4.3-macNOTEMar 04 2025

Exports:cPASSdiceCoefficientjaccardCoefficientoverlapScorerfTrainsimpsonCoefficientsvmTrainWeighted.matrixModel

Dependencies:backportsbitbit64bootbroomcaretclassclicliprclockcodetoolscolorspacecpp11crayondata.tablediagramdigestdplyre1071fansifarverforcatsforeachfuturefuture.applygenericsggplot2glmnetglobalsgluegowergtablehardhathavenhmsipredisobanditeratorsjomoKernSmoothlabelinglatticelavalifecyclelistenvlme4lubridatemagrittrMASSMatrixmgcvmiceminqamitmlModelMetricsmunsellnlmenloptrnnetnumDerivordinalpanparallellypillarpkgconfigplyrprettyunitspROCprodlimprogressprogressrproxyPRROCpurrrR6rangerrbibutilsRColorBrewerRcppRcppEigenRdpackreadrrecipesreformulasreshape2rlangrpartscalesshapesparsevctrsSQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetzdbucminfutf8vctrsviridisLitevroomwithr

Mass Spectrometry interaction Prediction (MSiP)

Rendered fromMSiP_tutorial.Rmdusingknitr::knitron Mar 04 2025.

Last update: 2021-06-18
Started: 2021-06-13

Readme and manuals

Help Manual

Help pageTopics
cPASScPASS
diceCoefficientdiceCoefficient
jaccardCoefficientjaccardCoefficient
overlapScoreoverlapScore
rfTrainrfTrain
Test data for scoringSampleDatInput
simpsonCoefficientsimpsonCoefficient
svmTrainsvmTrain
Test data for classifiertestdfClassifier
Weighted.matrixModelWeighted.matrixModel