Journal Article

CFGSCDSA: Predicting circRNA-drug sensitivity associations based on collaborative feature learning and graph structure learning

Zou, Quan, Zhang, Xue, Wang, Chunyu, Niu, Mengting

2026

Overview

The expression of circular RNAs (circRNAs) has been shown to be strongly correlated with drug sensitivity in human cells. However, experimental validation using wet-lab techniques is costly and inefficient, leaving a substantial portion of circRNA-drug sensitivity associations undiscovered. Therefore, improving the prediction efficiency of circRNA and sensitivity associations remains critical. Here, we describe a method that integrates collaborative feature learning and graph structure learning to predict associations between circRNAs and drug sensitivity (CFGSCDSA). Specifically, collaborative learning integrated heterogeneous features from diverse data sources, thereby addressing the issue of data sparsity. Furthermore, graph structure learning with a confidence-guided pseudo-labeling strategy was employed to mitigate the detrimental effect of excessive negative samples. Results: Experimental evaluation revealed that CFGSCDSA attained superior performance compared to all competing models. Moreover, case studies provided further evidence of its capability to accurately predict both novel associations and new drug-related links.

• Publisher: Public Library of Science,PLOS,Public Library of Science (PLoS)
• Subject: Biology and Life Sciences / Case studies / Computational Biology - methods / Drug sensitization / Engineering and Technology / Group work in education / Health aspects / Humans / Machine Learning / Medicine and Health Sciences / Methods / Missing observations (Statistics) / Pharmacology, Experimental / Physiological aspects / Research and Analysis Methods / RNA / RNA, Circular - genetics / RNA, Circular - metabolism / Team learning approach in education