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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).
| Version: | 1.0.0 |
| Imports: | mathjaxr |
| Published: | 2022-09-29 |
| DOI: | 10.32614/CRAN.package.NetInt |
| Author: | Giorgio Valentini [aut], Jessica Gliozzo [cre] |
| Maintainer: | Jessica Gliozzo <jessica.gliozzo at unimi.it> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| Citation: | NetInt citation info |
| Materials: | README |
| CRAN checks: | NetInt results |
| Reference manual: | NetInt.pdf |
| Package source: | NetInt_1.0.0.tar.gz |
| Windows binaries: | r-devel: NetInt_1.0.0.zip, r-release: NetInt_1.0.0.zip, r-oldrel: NetInt_1.0.0.zip |
| macOS binaries: | r-release (arm64): NetInt_1.0.0.tgz, r-oldrel (arm64): NetInt_1.0.0.tgz, r-release (x86_64): NetInt_1.0.0.tgz, r-oldrel (x86_64): NetInt_1.0.0.tgz |
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