Skip to content

Update README.md #3

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
mrbakhsh wants to merge 2 commits into
base: main
Choose a base branch
from
Open

Update README.md #3

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
c578cc4
Update README.md
mrbakhsh Sep 14, 2021
53cd954
Update README.md
mrbakhsh Sep 24, 2021
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
10 changes: 10 additions & 0 deletions README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,3 +1,13 @@
<!-- badges: start -->
[![](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 (&gt;=
3)](https://img.shields.io/badge/license-GPL%20(%3E=%203)-orange.svg)](https://cran.r-project.org/web/licenses/GPL%20(%3E=%203))
<!-- badges: end -->


# 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).

Expand Down