Implementation of modified mashup algorithm in Julia Language.
Switch branches/tags
Nothing to show
Clone or download
Pull request Compare This branch is 3 commits behind memoiry:master.
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Failed to load latest commit information.
docs
src
test
tools
.codecov.yml
.gitignore
.gitmodules
.travis.yml
LICENSE
README.md
REQUIRE
appveyor.yml
deploy.sh

README.md


Documentation Build Status Coverage Status License

This package provides real-time multiple association network integration algorithm for predicting gene function using both mashup and GeneMANIA.

We utilize Mashup algorithm to replace linear regression part of GeneMANIA, which is proved to be extremely computational efficient.

Usage

Installation

Pkg.rm("ModMashup")
Pkg.clone("https://github.com/memoiry/ModMashup.jl")

Example

See Example section in the documentation for more information about the package usage.

Integrate with R netDX package

See GSoC summary and end-to-end example for the information to update R netDX package with updated ModMashup.jl.

API

See Documentation for more information about API usage.

Background

Most successful computational approaches for protein function prediction integrate multiple genomics and proteomics data sources to make inferences about the function of unknown proteins. The most accurate of these algorithms have long running times, making them unsuitable for real-time protein function prediction in large genomes. As a result, the predictions of these algorithms are stored in static databases that can easily become outdated. Thus, GeneMANIA is proposed, that is as accurate as the leading methods, while capable of predicting protein function in real-time.

Reference

[1] Pai et al. (2016). preprint http://biorxiv.org/content/early/2016/10/31/084418

[2] Mostafavi et al. (2010). Bioinformatics. 26:1759. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2894508/

[3] Compact Integration of Multi-Network Topology for Functional Analysis of Genes. Cho, Hyunghoon et al. Cell Systems , Volume 3 , Issue 6 , 540 - 548.e5