ERFMTDA: Predicting tsRNA-disease associations using an enhanced rotative factorization machine

Abstract

MotivationtRNA-derived small RNAs (tsRNAs) have emerged as a novel class of regulatory molecules implicated in the pathogenesis of many human diseases, making them as promising biomarkers and therapeutic targets. However, existing computational methods for tsRNA-disease association prediction often overlook explicit biological attributes and complex feature interactions, limiting their predictive performance. ResultsWe propose ERFMTDA, an enhanced rotative factorization machine framework for predicting potential tsRNA-disease associations. ERFMTDA explicitly models complex interactions among heterogeneous biological features while integrating latent structural representations derived from the global association matrix. In addition, a biologically informed negative sampling strategy based on motif-level sequence similarity is introduced to improve the reliability of negative samples. Extensive experiments demonstrate that ERFMTDA consistently outperforms eleven state-of-the-art methods. Case studies on diabetic retinopathy and hepatocellular carcinoma further confirm its ability to prioritize biologically meaningful tsRNA-disease associations. Availability and implementationThe source codes and datasets of ERFMTDA are available at https://github.com/lanbiolab/ERFMTDA.