Feature Space Selection and Heterogeneous Effect Estimation for Blood-Brain Barrier Permeability: A Random Forest to the Generalized Random Forest Pipeline

arXiv:2609.29076 · stat.ML, cs.LG · Submitted 2026-09-24 · Read on arXiv

stat.ML, cs.LG

Submitted: 2026-09-24

Updated: 2026-09-24

Code: https://github.com/flash3105/Learning

Terminology

• AUC — 22× in this paper · explained in 2 episodes
• Ablation — 5× in this paper · explained in 1 episode
• Benchmark — 5× in this paper · explained in 2 episodes
• Dynamic Random Forest — 4× in this paper
• Accuracy — 3× in this paper · explained in 1 episode
• Conditional Average Treatment Effect — 3× in this paper
• Generalization — 3× in this paper · explained in 13 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Causal Inference — 2× in this paper · explained in 4 episodes
• Clustering — 2× in this paper · explained in 2 episodes
• Information Leakage — 2× in this paper · explained in 2 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Abundance — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Computational Biology — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Ensemble Learning — 1× in this paper · explained in 2 episodes
• External Validation — 1× in this paper · explained in 2 episodes
• F1 Score — 1× in this paper · explained in 3 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Parsimony — 1× in this paper · explained in 2 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes

Sources

• ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction
• Semi-Supervised Classification with Graph Convolutional Networks
• Molecular representation learning with language models and domain-relevant auxiliary tasks

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