piR-LGBM: A Sparse Autoencoder–Enhanced Gradient Boosting Framework to Uncover Disease-Associated piRNAs

Abstract

Piwi-interacting RNAs (piRNAs) are a distinctive category of single-stranded, noncoding RNAs that are crucial for regulating gene expression. Recent studies have shown that piRNAs have a major role in regulating germ and stem cell development, and their dysregulation is connected to various diseases. Consequently, precise identification of piRNA-disease correlations is crucial for understanding disease prognosis and therapy. Establishing the relationships between diseases and piRNAs through experimental research poses challenges and requires a substantial amount of cost and time. Computational approaches offer a promising alternative to mitigate these limitations. This study presents an ensemble approach piR-LGBM that relies on sparse autoencoder and Light Gradient Boosting Machine (LightGBM) classifier to uncover new associations between piRNAs and diseases. The proposed framework generates feature vectors by utilizing the information from piRNA sequences, disease semantics, and currently available piRNA-disease correlation data. The extracted features are then passed through a sparse autoencoder and subsequently to a LightGBM classifier for predicting novel piRNA-disease associations. piR-LGBM yielded an AUC of 0.9640 on fivefold cross-validation. We analyzed the performance of the model against leading methods and classifiers. The empirical results and case-based insights highlight piR-LGBM’s efficacy in identifying piRNA biomarkers behind diseases.