train_biomor

Tools for bioinformatics modeling using recursive transformer-inspired
architectures, autoencoders, random forests, XGBoost, and stacked ensemble models.
Includes utilities for cross-validation, calibration, benchmarking, and threshold
optimization in predictive modeling workflows. The methodology builds on ensemble
learning (Breiman 2001 <doi:10.1023/A:1010933404324>), gradient boosting (Chen and
Guestrin 2016 <doi:10.1145/2939672.2939785>), autoencoders (Hinton and Salakhutdinov
2006 <doi:10.1126/science.1127647>), and recursive transformer efficiency approaches
such as Mixture-of-Recursions (Bae et al. 2025 <doi:10.48550/arXiv.2507.10524>).

MD. Arshad

BioMoR

Bioinformatics Modeling with Recursion and Autoencoder-Based
Ensemble

train_biomor function

<dl><dt>data</dt>
<dd>Dataframe with numeric features + Label</dd>
<dt>feature_cols</dt>
<dd>Character vector of feature columns</dd>
<dt>epochs</dt>
<dd>Number of training epochs</dd>
<dt>batch_size</dt>
<dd>Batch size</dd>
<dt>lr</dt>
<dd>Learning rate</dd></dl>

Arguments

Train BioMoR Autoencoder — train_biomor

<dl>

<dt>data</dt>
<dd>Dataframe with numeric features + Label</dd>


<dt>feature_cols</dt>
<dd>Character vector of feature columns</dd>


<dt>epochs</dt>
<dd>Number of training epochs</dd>


<dt>batch_size</dt>
<dd>Batch size</dd>


<dt>lr</dt>
<dd>Learning rate</dd>

</dl>

train_biomor: Train BioMoR Autoencoder

Description

Usage

Value

Arguments