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NetInt
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]
[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. Accessed 29 Apr. 2025.
Previous versions and publish date:
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View NetInt citation profile
Other R packages that NetInt depends,
imports, suggests or enhances
Complete documentation for NetInt
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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