Bioinformatics Modeling with Recursion and Autoencoder-Based
Ensemble
Description
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 ), gradient boosting (Chen and
Guestrin 2016 ), autoencoders (Hinton and Salakhutdinov
2006 ), and recursive transformer efficiency approaches
such as Mixture-of-Recursions (Bae et al. 2025 ).