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

Aiswarya Mohan¹, Deepthi K¹

¹Department of Computer Science, Central University of Kerala (Govt. of India), Kasaragod, Kerala, India.

Omics : a Journal of Integrative Biology

|June 10, 2026

Summary

This study introduces piR-LGBM, a computational method for identifying links between Piwi-interacting RNAs (piRNAs) and diseases. The approach uses machine learning to predict novel piRNA-disease associations, aiding disease prognosis and therapy.

Keywords:

disease lightGBM piwi-interacting RNA sparse autoencoder

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Piwi-interacting RNAs (piRNAs) are noncoding RNAs vital for gene regulation, germ, and stem cell development.
Dysregulation of piRNAs is linked to various diseases, making piRNA-disease correlation identification crucial for prognosis and therapy.
Experimental identification of piRNA-disease relationships is costly and time-consuming.

Purpose of the Study:

To develop a computational approach for uncovering novel associations between piRNAs and diseases.
To mitigate the limitations of experimental methods in identifying piRNA-disease correlations.

Main Methods:

An ensemble approach, piR-LGBM, was developed, integrating sparse autoencoder and Light Gradient Boosting Machine (LightGBM) classifier.
Feature vectors were generated using piRNA sequences, disease semantics, and existing piRNA-disease correlation data.
The framework predicts novel piRNA-disease associations by processing extracted features through a sparse autoencoder and LightGBM classifier.

Main Results:

The piR-LGBM model achieved an Area Under the Curve (AUC) of 0.9640 on fivefold cross-validation.
Performance was evaluated against leading computational methods and classifiers.
Empirical results and case studies demonstrated the model's effectiveness in identifying piRNA biomarkers associated with diseases.

Conclusions:

The piR-LGBM framework offers an efficient computational alternative for identifying piRNA-disease associations.
The study highlights the potential of piR-LGBM in discovering novel piRNA biomarkers for disease understanding and treatment.
Accurate identification of piRNA-disease links is essential for advancing disease prognosis and therapeutic strategies.