TREPP: Tandem Repeat Expansion Pathogenicity Prediction via Stacked CatBoost and Context-Aware Sequence Features

Predicting the pathogenic potential of tandem repeat (TR) expansions is critical for understanding neurogenetic disorders but remains challenging due to data scarcity, extreme class imbalance, and complex sequence dependencies. Most existing approaches primarily rely on gene-centric signals, often overlooking intrinsic sequence properties that mechanistically modulate instability. This paper proposes TREPP, an interpretable ensemble learning framework designed to prioritize candidate high-risk TR loci. TREPP combines genomic location information with local sequence properties to represent the biological context of expansion. First, the framework extracts a comprehensive feature set including gene proximity, GC content differences, and local repeat density patterns. Then, to mitigate label scarcity, TREPP employs a multi-sampling strategy that trains multiple CatBoost base learners on diversified negative subsets. Finally, a sparse logistic regression meta-learner aggregates the calibrated outputs of these base models to produce a final prioritization score. Experimental results on a curated benchmark dataset show that TREPP achieves an AUPRC of 94.71%, exceeding the state-of-the-art method RExPRT by 6.11 percentage points on the fixed benchmark split. Furthermore, interpretability and bias-control analyses support the interpretation that TREPP leverages biologically plausible signals, making it a useful prioritization framework for identifying candidate high-risk TR loci. TREPP is publicly available at https://github.com/minghuaxu/TREPP.