AutoEdge-CCP: A novel approach for predicting cancer-associated circRNAs and drugs based on automated edge embedding

Abstract

The unique expression patterns of circRNAs linked to the advancement and prognosis of cancer underscore their considerable potential as valuable biomarkers. Repurposing existing drugs for new indications can significantly reduce the cost of cancer treatment. Computational prediction of circRNA-cancer and drug-cancer relationships is crucial for precise cancer therapy. However, prior computational methods fail to analyze the interaction between circRNAs, drugs, and cancer at the systematic level. It is essential to propose a method that uncover more valuable information for achieving cancer-centered multi-association prediction. In this paper, we present a novel computational method, AutoEdge-CCP, to unveil cancer-associated circRNAs and drugs. We abstract the complex relationships between circRNAs, drugs, and cancer into a multi-source heterogeneous network. In this network, each molecule is represented by two types information, one is the intrinsic attribute information of molecular features, and the other is the link information explicitly modeled by autoGNN, which searches information from both intra-layer and inter-layer of message passing neural network. The significant performance on multi-scenario applications and case studies establishes AutoEdge-CCP as a potent and promising association prediction tool.

DiSMVC: a multi-view graph collaborative learning framework for measuring disease similarity

“…Graph representation is designed to encode and extract meaningful embeddings preserving intricate structural properties from graph-structured data, and has been successfully applied in various bioinformatics tasks, such as circRNA-disease association detection ( Wang et al 2020 , Chen et al 2024 , Niu et al 2024 ), drug–target binding affinity prediction ( Öztürk et al 2018 ), cell–cell interaction identification ( Yang et al 2023 ). Inspired by the powerful ability of graph neural networks to uncover hidden semantic knowledge from bio-entity networks ( Zhou et al 2020 , Yi et al 2022 ), we adopt two graph encoders including Graph Convolutional Networks (GCN) ( Kipf and Welling 2017 ) and Graph Attention Networks (GAT) ( Veličković et al 2018 ) for exploring the latent interaction pattern from each homogeneous bio-network.…”