train_dia

A training dataset for diagnostic models, containing sample IDs,
binary outcomes, and gene expression features.

datasets

Provides a framework to build and evaluate diagnosis or
prognosis models using stacking, voting, and bagging ensemble
techniques with various base learners. The package also includes
tools for visualization and interpretation of models. The development
version of the package is available on 'GitHub' at
<https://github.com/xiaojie0519/E2E>. The methods
are based on the foundational work of Breiman (1996) <doi:10.1007/BF00058655>
on bagging and Wolpert (1992) <doi:10.1016/S0893-6080(05)80023-1>
on stacking.

Shanjie Luan

Ensemble Learning Framework for Diagnostic and Prognostic
Modeling

train_dia function

A data frame with rows for samples and 22 columns:<dl>
<dt>sample</dt>
<dd>character. Unique identifier for each sample.</dd><dt>outcome</dt>
<dd>integer. The binary outcome, where 1 typically represents a positive case and 0 a negative case.</dd><dt>AC004637.1</dt>
<dd>numeric. Gene expression level.</dd><dt>AC008459.1</dt>
<dd>numeric. Gene expression level.</dd><dt>AC009242.1</dt>
<dd>numeric. Gene expression level.</dd><dt>AC016735.1</dt>
<dd>numeric. Gene expression level.</dd><dt>AC090125.1</dt>
<dd>numeric. Gene expression level.</dd><dt>AC104237.3</dt>
<dd>numeric. Gene expression level.</dd><dt>AC112721.2</dt>
<dd>numeric. Gene expression level.</dd><dt>AC246817.1</dt>
<dd>numeric. Gene expression level.</dd><dt>AL135841.1</dt>
<dd>numeric. Gene expression level.</dd><dt>AL139241.1</dt>
<dd>numeric. Gene expression level.</dd><dt>HYMAI</dt>
<dd>numeric. Gene expression level.</dd><dt>KCNIP2.AS1</dt>
<dd>numeric. Gene expression level.</dd><dt>LINC00639</dt>
<dd>numeric. Gene expression level.</dd><dt>LINC00922</dt>
<dd>numeric. Gene expression level.</dd><dt>LINC00924</dt>
<dd>numeric. Gene expression level.</dd><dt>LINC00958</dt>
<dd>numeric. Gene expression level.</dd><dt>LINC01028</dt>
<dd>numeric. Gene expression level.</dd><dt>LINC01614</dt>
<dd>numeric. Gene expression level.</dd><dt>LINC01644</dt>
<dd>numeric. Gene expression level.</dd><dt>PRDM16.DT</dt>
<dd>numeric. Gene expression level.</dd>
</dl>

Format

Training Data for Diagnostic Models — train_dia

A data frame with rows for samples and 22 columns:<dl>
<dt>sample</dt>
<dd>character. Unique identifier for each sample.</dd>

<dt>outcome</dt>
<dd>integer. The binary outcome, where 1 typically represents a positive case and 0 a negative case.</dd>

<dt>AC004637.1</dt>
<dd>numeric. Gene expression level.</dd>

<dt>AC008459.1</dt>
<dd>numeric. Gene expression level.</dd>

<dt>AC009242.1</dt>
<dd>numeric. Gene expression level.</dd>

<dt>AC016735.1</dt>
<dd>numeric. Gene expression level.</dd>

<dt>AC090125.1</dt>
<dd>numeric. Gene expression level.</dd>

<dt>AC104237.3</dt>
<dd>numeric. Gene expression level.</dd>

<dt>AC112721.2</dt>
<dd>numeric. Gene expression level.</dd>

<dt>AC246817.1</dt>
<dd>numeric. Gene expression level.</dd>

<dt>AL135841.1</dt>
<dd>numeric. Gene expression level.</dd>

<dt>AL139241.1</dt>
<dd>numeric. Gene expression level.</dd>

<dt>HYMAI</dt>
<dd>numeric. Gene expression level.</dd>

<dt>KCNIP2.AS1</dt>
<dd>numeric. Gene expression level.</dd>

<dt>LINC00639</dt>
<dd>numeric. Gene expression level.</dd>

<dt>LINC00922</dt>
<dd>numeric. Gene expression level.</dd>

<dt>LINC00924</dt>
<dd>numeric. Gene expression level.</dd>

<dt>LINC00958</dt>
<dd>numeric. Gene expression level.</dd>

<dt>LINC01028</dt>
<dd>numeric. Gene expression level.</dd>

<dt>LINC01614</dt>
<dd>numeric. Gene expression level.</dd>

<dt>LINC01644</dt>
<dd>numeric. Gene expression level.</dd>

<dt>PRDM16.DT</dt>
<dd>numeric. Gene expression level.</dd>


</dl>

train_dia: Training Data for Diagnostic Models

Description

Usage

Arguments

Format

Details