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: | 1.2.2 |
| Depends: | R (≥ 3.5.0) |
| Imports: | Biostrings,caret,data.table,dplyr,GenomicAlignments,IRanges,mltools,randomForest,Rsamtools, stats,xgboost,MTPS,stringr,R.utils,Rdpack |
| Suggests: | rmarkdown,testthat (≥ 3.0.0),knitr,httr |
| Published: | 2025-12-04 |
| DOI: | 10.32614/CRAN.package.iimi |
| Author: | Haochen Ning [aut], Ian Boyes [aut], Ibrahim Numanagić [aut], Michael Rott [aut], Li Xing [aut], Xuekui Zhang [aut, cre] |
| Maintainer: | Xuekui Zhang <xuekui at uvic.ca> |
| License: | MIT + fileLICENSE |
| NeedsCompilation: | no |
| CRAN checks: | iimi results |
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