From c578cc410cf047c633b25c2f635e6a7f62d7b70d Mon Sep 17 00:00:00 2001 From: Matineh Rahmatbakhsh Date: Tue, 14 Sep 2021 15:32:07 -0600 Subject: [PATCH 1/2] Update README.md --- README.md | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/README.md b/README.md index 63211ab..713b723 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,8 @@ + +[![CRAN Version](https://www.r-pkg.org/badges/version/MSiP)](https://cran.r-project.org/package=MSiP) +[![Downloads from the RStudio CRAN mirror](https://cranlogs.r-pkg.org/badges/MSiP)](https://cranlogs.r-pkg.org/badges/MSiP) + + # Mass Spectrometry interaction Prediction (MSiP) The MSiP is a computational approach to predict protein-protein interactions from largescale 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 [Drew et al., 2017](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5488662). From 53cd9540e8330085caf33a9c292659a6515f2712 Mon Sep 17 00:00:00 2001 From: Matineh Rahmatbakhsh Date: Fri, 24 Sep 2021 14:53:23 -0600 Subject: [PATCH 2/2] Update README.md --- README.md | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/README.md b/README.md index 713b723..a1b4deb 100644 --- a/README.md +++ b/README.md @@ -1,8 +1,13 @@ +[![](https://img.shields.io/badge/lifecycle-stable-yellow.svg)](https://lifecycle.r-lib.org/articles/stages.html#stable) +[![](https://img.shields.io/github/last-commit/mrbakhsh/MSiP.svg)](https://github.com/mrbakhsh/MSiP/commits/main) [![CRAN Version](https://www.r-pkg.org/badges/version/MSiP)](https://cran.r-project.org/package=MSiP) [![Downloads from the RStudio CRAN mirror](https://cranlogs.r-pkg.org/badges/MSiP)](https://cranlogs.r-pkg.org/badges/MSiP) +[![License: GPL (>= +3)](https://img.shields.io/badge/license-GPL%20(%3E=%203)-orange.svg)](https://cran.r-project.org/web/licenses/GPL%20(%3E=%203)) + # Mass Spectrometry interaction Prediction (MSiP) The MSiP is a computational approach to predict protein-protein interactions from largescale 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 [Drew et al., 2017](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5488662).