Bioinformatics Modeling with Recursion and Autoencoder-Based Ensemble

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 ).