predict_iimi

Uses a machine learning model to predict the infection status
for the plant sample(s). User can use their own model if needed.

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.

Xuekui Zhang

iimi

Identifying Infection with Machine Intelligence

Haochen Ning

Ian Boyes

Ibrahim Numanagić

Michael Rott

Li Xing

predict_iimi function

<dl><dt>newdata</dt>
<dd>A matrix or data frame that contains the features extracted
from the coverage profile using <code>convert_bam_to_cov()</code>.</dd>
<dt>method</dt>
<dd>The machine learning method of choice, <code>rf</code>,
<code>xgb</code>, or <code>en</code>. <code>rf</code> stands for random forest model; <code>xgb</code> stands for
XGBoost model; and <code>en</code> stands for elastic net model.</dd>
<dt>trained_model</dt>
<dd>The trained model. If not provided, default model is
used.</dd>
<dt>report_virus_level</dt>
<dd>If <code>TRUE</code>, the function returns the aggregated
results based on the virus. If <code>FALSE</code>, the function returns the
unaggregated results based on segment level with each decision's
probability decided by the model. We do not recommended to set this to
<code>FALSE</code>.</dd></dl>

Arguments

predict_iimi() — predict_iimi

<dl>

<dt>newdata</dt>
<dd>A matrix or data frame that contains the features extracted
from the coverage profile using <code>convert_bam_to_cov()</code>.</dd>


<dt>method</dt>
<dd>The machine learning method of choice, <code>rf</code>,
<code>xgb</code>, or <code>en</code>. <code>rf</code> stands for random forest model; <code>xgb</code> stands for
XGBoost model; and <code>en</code> stands for elastic net model.</dd>


<dt>trained_model</dt>
<dd>The trained model. If not provided, default model is
used.</dd>


<dt>report_virus_level</dt>
<dd>If <code>TRUE</code>, the function returns the aggregated
results based on the virus. If <code>FALSE</code>, the function returns the
unaggregated results based on segment level with each decision's
probability decided by the model. We do not recommended to set this to
<code>FALSE</code>.</dd>

</dl>

predict_iimi: predict_iimi()

Description

Usage

Value

Arguments

Examples