iimi: Identifying Infection with Machine Intelligence

A novel machine learning method for plant viruses diagnostic using genome sequencing data. This package includes three different machine learning models, random forest, XGBoost, and elastic net, to train and predict mapped genome samples. Mappability profile and unreliable regions are introduced to the algorithm, and users can build a mappability profile from scratch with functions included in the package. Plotting mapped sample coverage information is provided.

Version: 1.0.2
Depends: R (≥ 3.5.0)
Imports: GenomicAlignments, IRanges, Rsamtools, data.table, mltools, randomForest, xgboost, Biostrings, stats, MTPS, R.utils, caret, stringr, dplyr
Suggests: testthat (≥ 3.0.0), knitr, rmarkdown
Published: 2024-03-07
Author: Haochen Ning [aut], Ian Boyes [aut], Ibrahim Numanagić ORCID iD [aut], Michael Rott [aut], Li Xing ORCID iD [aut], Xuekui Zhang ORCID iD [aut, cre]
Maintainer: Xuekui Zhang <xuekui at uvic.ca>
License: MIT + file LICENSE
NeedsCompilation: no
CRAN checks: iimi results


Reference manual: iimi.pdf
Vignettes: Introduction to the iimi package


Package source: iimi_1.0.2.tar.gz
Windows binaries: r-prerel: iimi_1.0.2.zip, r-release: iimi_1.0.2.zip, r-oldrel: iimi_1.0.2.zip
macOS binaries: r-prerel (arm64): iimi_1.0.2.tgz, r-release (arm64): iimi_1.0.2.tgz, r-oldrel (arm64): iimi_1.0.2.tgz, r-prerel (x86_64): iimi_1.0.2.tgz, r-release (x86_64): iimi_1.0.2.tgz


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