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

## TL;DR

This paper introduces CFGSCDSA, a computational method that uses collaborative and graph learning to predict how circular RNAs affect drug sensitivity in cells, offering a faster alternative to costly lab experiments.

## Contribution

The novel CFGSCDSA framework combines collaborative feature learning and confidence-guided graph structure learning to enhance prediction of circRNA–drug sensitivity associations.

## Key findings

- CFGSCDSA outperforms existing methods in predicting circRNA–drug sensitivity associations.
- The method effectively handles data sparsity and false-negative samples using collaborative and graph learning strategies.
- Case studies confirm CFGSCDSA's ability to identify both known and novel circRNA–drug associations.

## Abstract

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.

Circular RNAs (circRNAs) play important roles in regulating drug sensitivity, but identifying their associations with specific drugs through biological experiments is costly and time-consuming. Computational methods therefore provide an efficient alternative for uncovering these potential relationships. In this study, we propose CFGSCDSA, a predictive framework that integrates collaborative feature learning with graph structure learning to improve the accuracy of circRNA–drug sensitivity association prediction. The collaborative feature-learning module effectively merges heterogeneous information from multiple biological sources, addressing data sparsity and enrich feature representations. A graph structure learning strategy guided by confidence-based pseudo-labeling strategy is employed to refine graph topology and mitigate the negative impact of false-negative samples. Extensive experiments demonstrate that CFGSCDSA outperforms existing state-of-the-art methods across multiple evaluation metrics. Furthermore, case studies verify its capability to uncover both known and novel circRNA–drug sensitivity associations, highlighting its potential to facilitate the discovery of therapeutic biomarkers.