Package: NetInt
Type: Package
Title: Methods for Unweighted and Weighted Network Integration
Version: 1.0.2
Authors@R: c(
    person("Giorgio", "Valentini", email = "valentini@di.unimi.it", role = "aut"),
    person("Jessica", "Gliozzo", email = "jessica.gliozzo@gmail.com", role = "cre"))
Maintainer: Jessica Gliozzo <jessica.gliozzo@gmail.com>
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).
License: GPL (>= 2)
Encoding: UTF-8
Config/roxygen2/version: 8.1.0
Repository: https://gliozzoj.r-universe.dev
Date/Publication: 2026-09-09 14:59:41 UTC
RemoteUrl: https://github.com/GliozzoJ/NetInt
RemoteRef: HEAD
RemoteSha: 4e8a71180d289839579d874dc6b81187c910123c
NeedsCompilation: no
Packaged: 2026-10-09 10:44:35 UTC; root
Author: Giorgio Valentini [aut],
  Jessica Gliozzo [cre]
Built: R 4.6.0; ; 2026-10-09 10:46:10 UTC; unix
