Accurate prediction of candidate lncRNAs associated with DNA damage response based on gene expression patterns from graph neural networks.

Motivation: DNA damage response (DDR) is essential for maintaining genome stability and preventing tumorigenesis. While protein-coding DDR genes have been extensively investigated, long non-coding RNAs (lncRNAs) remain relatively understudied despite the growing evidence of their involvement in DDR. Particularly, it is rather challenging to systematically identify DDR-associated lncRNAs through experimental approaches, which are often time-consuming, labor-intensive, and expensive. Moreover, lncRNAs lack translational open reading frames often targeted by experimental methods. Results: In this study, we have developed a new machine learning approach, GlncDDR, which utilizes graph-based node embedding of gene expression features and supervised learning algorithms to predict candidate lncRNAs associated with DDR. GlncDDR models achieved robust predictive performance with ROC-AUC reaching ∼0.93 on test data. We used the models to predict 1232 candidate lncRNAs, including several known DDR regulators such as Availability: The source code and datasets used in the study are available at https://github.com/BioDataLearning/GlncDDR.git.