Stage2_FeartureSelection

Biologically Explainable Machine Learning Framework for Phenotype Prediction using omics data described in Chen and Schwarz (2017) <doi:10.48550/arXiv.1712.00336>.Identifying reproducible and interpretable biological patterns from high-dimensional omics data is a critical factor in understanding the risk mechanism of complex disease. As such, explainable machine learning can offer biological insight in addition to personalized risk scoring.In this process, a feature space of biological pathways will be generated, and the feature space can also be subsequently analyzed using WGCNA (Described in Horvath and Zhang (2005) <doi:10.2202/1544-6115.1128> and Langfelder and Horvath (2008) <doi:10.1186/1471-2105-9-559> ) methods.

Shunjie Zhang

BioM2

Biologically Explainable Machine Learning Framework

Junfang Chen

Stage2_FeartureSelection function

<dl><dt>Stage2_FeartureSelection_Method</dt>
<dd>Feature selection methods. Available options are
c(NULL, 'cor', 'wilcox.test','cor_rank','wilcox.test_rank','RemoveHighcor', 'RemoveLinear').</dd>
<dt>data</dt>
<dd>The input training dataset. The first column is the label.</dd>
<dt>label</dt>
<dd>The label of dataset</dd>
<dt>cutoff</dt>
<dd>The cutoff used for feature selection threshold. It can be any value
between 0 and 1.</dd>
<dt>preMode</dt>
<dd>The prediction mode. "Currently only supports 'probability' for binary classification tasks."</dd>
<dt>classifier</dt>
<dd>Learners in mlr3</dd>
<dt>verbose</dt>
<dd>Whether to print running process information to the console</dd>
<dt>cores</dt>
<dd>The number of cores used for computation.</dd></dl>

Arguments

Author

Stage 2 Fearture Selection — Stage2_FeartureSelection

<dl>

<dt>Stage2_FeartureSelection_Method</dt>
<dd>Feature selection methods. Available options are
c(NULL, 'cor', 'wilcox.test','cor_rank','wilcox.test_rank','RemoveHighcor', 'RemoveLinear').</dd>


<dt>data</dt>
<dd>The input training dataset. The first column is the label.</dd>


<dt>label</dt>
<dd>The label of dataset</dd>


<dt>cutoff</dt>
<dd>The cutoff used for feature selection threshold. It can be any value
between 0 and 1.</dd>


<dt>preMode</dt>
<dd>The prediction mode. "Currently only supports 'probability' for binary classification tasks."</dd>


<dt>classifier</dt>
<dd>Learners in mlr3</dd>


<dt>verbose</dt>
<dd>Whether to print running process information to the console</dd>


<dt>cores</dt>
<dd>The number of cores used for computation.</dd>

</dl>

Stage2_FeartureSelection: Stage 2 Fearture Selection

Description

Usage

Value

Arguments

Author