Bioinformatics Modeling with Recursion and Autoencoder-Based Ensemble
Benchmark a trained model
Run full BioMoR pipeline
BioMoR: Bioinformatics Modeling with Recursion, Autoencoders, and Stac...
Compute Brier Score
Calibrate model probabilities
Compute optimal threshold for maximum F1 score
Get caret cross-validation control
Get Embeddings from Autoencoder (stub)
Prepare dataset for modeling
Train Autoencoder (stub)
Train BioMoR Autoencoder
Train a Random Forest model with caret
Train an XGBoost model with caret
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>).