LLM4PDA: Augmenting piRNA-Disease Association Prediction with Large Language Models.

Abstract

PIWI-interacting RNAs (piRNAs) maintain genomic stability by interacting with PIWI proteins and have been implicated in various human diseases. However, wet-lab experiments remain limited in throughput, creating an urgent need for computational methods to systematically identify piRNA-disease associations. Although numerous computational approaches have been proposed, they still face major challenges such as limited feature representations and insufficient integration of biological knowledge. This study presents LLM4PDA, a large language model-augmented framework for piRNA-disease association prediction. PiRNA sequences are encoded using RNA-FM, whereas disease representations are derived from LLM-generated disease descriptions guided by a reusable, structured prompt template and encoded using OpenAI's text-embedding models. These representations are mapped into a common latent space through an MLP-based alignment module, enabling association scoring for candidate piRNA-disease pairs. Separately from the predictive model, a post-prediction interpretive module organizes high-confidence predictions into structured, knowledge-informed biological summaries to support hypothesis generation and guide downstream experimental validation. Five-fold cross-validation demonstrates that LLM4PDA achieves area under the curve (AUC) values of 0.984 and 0.985 on two benchmark datasets, outperforming eight state-of-the-art methods. Case studies further illustrate how the interpretive module facilitates structured synthesis of biological context for selected high-confidence associations.