Package: iimi 1.2.1

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.

Authors:Haochen Ning [aut], Ian Boyes [aut], Ibrahim Numanagić [aut], Michael Rott [aut], Li Xing [aut], Xuekui Zhang [aut, cre]

iimi_1.2.1.tar.gz
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iimi.pdf |iimi.html
iimi/json (API)

# Install 'iimi' in R:
install.packages('iimi', repos = c('https://ubcxzhang.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:
  • example_cov - Coverage profiles of three plant samples.
  • example_diag - Known diagnostics result of virus segments
  • nucleotide_info - Nucleotide information of virus segments
  • trained_en - A trained model using the default Elastic Net settings
  • trained_rf - A trained model using the default Random Forest settings
  • trained_xgb - A trained model using the default XGBoost settings
  • unreliable_regions - The unreliable regions of the virus segments

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.48 score 5 scripts 517 downloads 7 exports 123 dependencies

Last updated 5 months agofrom:5faed8fe98. Checks:8 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKApr 01 2025
R-4.5-winOKApr 01 2025
R-4.5-macOKApr 01 2025
R-4.5-linuxOKApr 01 2025
R-4.4-winOKApr 01 2025
R-4.4-macOKApr 01 2025
R-4.4-linuxOKApr 01 2025
R-4.3-winOKMar 02 2025

Exports:convert_bam_to_rleconvert_rle_to_dfcreate_high_nucleotide_contentcreate_mappability_profileplot_covpredict_iimitrain_iimi

Dependencies:abindaskpassBHBiobaseBiocGenericsBiocParallelBiostringsbitopscaretclasscliclockcodetoolscolorspacecpp11crayoncurldata.tableDelayedArraydiagramdigestdplyre1071fansifarverforeachformatRfutile.loggerfutile.optionsfuturefuture.applygenericsGenomeInfoDbGenomeInfoDbDataGenomicAlignmentsGenomicRangesggplot2glmnetglobalsgluegowergtablehardhathttripredIRangesisobanditeratorsjsonliteKernSmoothlabelinglambda.rlatticelavalifecyclelistenvlubridatemagrittrMASSMatrixMatrixGenericsmatrixStatsmgcvmimemltoolsModelMetricsMTPSmunsellnlmennetnumDerivopensslparallellypillarpkgconfigplyrpROCprodlimprogressrproxypurrrR.methodsS3R.ooR.utilsR6randomForestrbibutilsRColorBrewerRcppRcppEigenRdpackrecipesreshape2RhtslibrlangrpartRsamtoolsS4ArraysS4VectorsscalesshapesnowSparseArraysparsevctrsSQUAREMstringistringrSummarizedExperimentsurvivalsystibbletidyrtidyselecttimechangetimeDatetzdbUCSC.utilsutf8vctrsviridisLitewithrxgboostXVector

Introduction to the iimi package

Rendered frompackage_vignette.Rmdusingknitr::rmarkdownon Apr 01 2025.

Last update: 2024-11-01
Started: 2024-03-08