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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 
[aut],
Michael Rott [aut],
Li Xing [aut],
Xuekui Zhang
[aut, cre] |
| Maintainer: |
Xuekui Zhang <xuekui at uvic.ca> |
| License: |
MIT + file LICENSE |
| NeedsCompilation: |
no |
| CRAN checks: |
iimi results |
Documentation:
Downloads:
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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