HGECDA: A Heterogeneous Graph Embedding Model for CircRNA-Disease Association Prediction

Fu, Yao; Yang, Runtao; Zhang, Lina; Xu, Fang

doi:10.1109/jbhi.2023.3299042

Cited by 3 publications

(2 citation statements)

References 40 publications

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“…This approach centers on the amalgamation of varied biological data sources into an extensive network graph, aiming to attain a deeper comprehension of the intricate nature of biological systems. These graphs assist researchers in identifying potential biomarkers, 43 deciphering disease mechanisms, 44 and pinpointing drug targets. 45 For instance, Hetionet 46 network integrated diverse bioinformatics data from various public resources and predicted the therapeutic probabilities of 209,168 compound− disease pairs.…”

Section: ■ Introductionmentioning

confidence: 99%

HANSynergy: Heterogeneous Graph Attention Network for Drug Synergy Prediction

Cheng,

Wang,

Liu

et al. 2024

J. Chem. Inf. Model.

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Drug synergy therapy is a promising strategy for cancer treatment. However, the extensive variety of available drugs and the time-intensive process of determining effective drug combinations through clinical trials pose significant challenges. It requires a reliable method for the rapid and precise selection of drug synergies. In response, various computational strategies have been developed for predicting drug synergies, yet the exploitation of heterogeneous biological network features remains underexplored. In this study, we construct a heterogeneous graph that encompasses diverse biological entities and interactions, utilizing rich data sets from sources, such as DrugCombDB, PubChem, UniProt, and cancer cell line encyclopedia (CCLE). We initialize node feature representations and introduce a novel virtual node to enhance drug representation. Our proposed method, the heterogeneous graph attention network for drug−drug synergy prediction (HANSynergy), has been experimentally validated to demonstrate that the heterogeneous graph attention network can extract key node features, efficiently harness the diversity of information, and further enhance network functionality through the incorporation of a multihead attention mechanism. In the comparative experiment, the highest accuracy (Acc) and area under the curve (AUC) are 0.877 and 0.947, respectively, in DrugCombDB_early data set, demonstrating the superiority of HANSynergy over the competing methods. Moreover, protein− protein interactions are important in understanding the mechanism of action of drugs. The heterogeneous attention mechanism facilitates protein−protein interaction analysis. By analyzing the changes of attention weight before and after heterogeneous network training, we investigated proteins that may be associated with drug combinations. Additionally, case studies align our findings with existing research, underscoring the potential of HANSynergy in drug synergy prediction. This advancement not only contributes to the burgeoning field of drug synergy prediction but also holds the potential to provide valuable insights and uncover new drug synergies for combating cancer.

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Section: ■ Introductionmentioning

confidence: 99%

HANSynergy: Heterogeneous Graph Attention Network for Drug Synergy Prediction

Cheng,

Wang,

Liu

et al. 2024

J. Chem. Inf. Model.

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“…Several researches have been developed to predict the potential circRNA-disease association. For example, Fu et al (2023) proposed a graph embedding method to predict the potential circRNA-disease association based on a constructed circRNA-miRNA-disease heterogeneous network (HGECDA). Using meta-path-based random walks in HGECDA, they captured interactions among circRNA, miRNA, and disease nodes.…”

Section: Introductionmentioning

confidence: 99%

Three-layer heterogeneous network based on the integration of CircRNA information for MiRNA-disease association prediction

Qu,

Liu,

et al. 2024

PeerJ Computer Science

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Increasing research has shown that the abnormal expression of microRNA (miRNA) is associated with many complex diseases. However, biological experiments have many limitations in identifying the potential disease-miRNA associations. Therefore, we developed a computational model of Three-Layer Heterogeneous Network based on the Integration of CircRNA information for MiRNA-Disease Association prediction (TLHNICMDA). In the model, a disease-miRNA-circRNA heterogeneous network is built by known disease-miRNA associations, known miRNA-circRNA interactions, disease similarity, miRNA similarity, and circRNA similarity. Then, the potential disease-miRNA associations are identified by an update algorithm based on the global network. Finally, based on global and local leave-one-out cross validation (LOOCV), the values of AUCs in TLHNICMDA are 0.8795 and 0.7774. Moreover, the mean and standard deviation of AUC in 5-fold cross-validations is 0.8777+/−0.0010. Especially, the two types of case studies illustrated the usefulness of TLHNICMDA in predicting disease-miRNA interactions.

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CircRNA-based therapeutics: Current opinions and clinical potential

Liu,

Yao,

Zhou

et al. 2024

TIME

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<p>Circular RNAs (circRNAs) are single-stranded, covalently closed RNA molecules that perform diverse roles in various cellular processes and have been implicated in many pathological conditions. Owing to their intrinsic stability and low immunogenicity, circRNAs have garnered significant interest for their therapeutic potential in multiple diseases, with advancements in efficient in vitro production methods and optimized delivery systems. In this review, we provide a comprehensive overview of current knowledge on circRNA biogenesis and functions, and summarize recent advances in various technologies for circRNA research, including their profiling, validation, and biosynthesis. We also discuss key delivery strategies and therapeutic applications, highlighting the promising prospects and current challenges for the clinical development of circRNA-based therapeutics. Research to date has shown that circRNAs are not merely splicing errors and that circRNA-based therapeutic platforms may have superior application prospects from bench to bedside.</p>

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HGECDA: A Heterogeneous Graph Embedding Model for CircRNA-Disease Association Prediction

Cited by 3 publications

References 40 publications

HANSynergy: Heterogeneous Graph Attention Network for Drug Synergy Prediction

HANSynergy: Heterogeneous Graph Attention Network for Drug Synergy Prediction

Three-layer heterogeneous network based on the integration of CircRNA information for MiRNA-disease association prediction

CircRNA-based therapeutics: Current opinions and clinical potential

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