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NetInt  

Methods for Unweighted and Weighted Network Integration
View on CRAN: Click here


Download and install NetInt package within the R console
Install from CRAN:
install.packages("NetInt")

Install from Github:
library("remotes")
install_github("cran/NetInt")

Install by package version:
library("remotes")
install_version("NetInt", "1.0.0")



Attach the package and use:
library("NetInt")
Maintained by
Jessica Gliozzo
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2022-09-29
Latest Update: 2022-09-29
Description:
Implementation of network integration approaches comprising unweighted and weighted integration methods. Unweighted integration is performed considering the average, per-edge average, maximum and minimum of networks edges. Weighted integration takes into account a weight for each network during the fusion process, where the weights express the ''predictiveness strength'' of each network considering a specific predictive task. Weights can be learned using a machine learning algorithm able to associate the weights to the assessment of the accuracy of the learning algorithm trained on the network itself. The implemented methods can be applied to effectively integrate different biological networks modelling a wide range of problems in bioinformatics (e.g. disease gene prioritization, protein function prediction, drug repurposing, clinical outcome prediction).
How to cite:
Jessica Gliozzo (2022). NetInt: Methods for Unweighted and Weighted Network Integration. R package version 1.0.0, https://cran.r-project.org/web/packages/NetInt
Previous versions and publish date:
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Other R packages that NetInt depends, imports, suggests or enhances
Functions, R codes and Examples using the NetInt R package
Some associated functions: ATLEASTK.int . MAX.int . MIN.int . MS.UA.int . PUA.int . UA.int . WA.int . WAP.int . align.networks . lalign.networks . 
Some associated R codes: netint.R .  Full NetInt package functions and examples
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