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iimi (version 1.2.2)

Identifying Infection with Machine Intelligence

Description

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.

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Version

Install

install.packages('iimi')

Monthly Downloads

273

Version

1.2.2

License

MIT + file LICENSE

Maintainer

Xuekui Zhang

Last Published

December 4th, 2025

Functions in iimi (1.2.2)

convert_rle_to_df

Convert run-length encodings (RLEs) to a data frame.
nucleotide_info

Nucleotide information of virus segments
create_mappability_profile

create_mappability_profile
create_high_nucleotide_content

create_high_nucleotide_content
train_iimi

train_iimi()
plot_cov

plot_cov()
convert_bam_to_rle

convert_bam_to_rle
predict_iimi

predict_iimi()
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
example_cov

Coverage profiles of three plant samples.
trained_en

A trained model using the default Elastic Net settings
example_diag

Known diagnostics result of virus segments