Line graph attention networks for predicting disease-associated Piwi-interacting RNAs

Zheng, Kai; Zhang, Xinlu; Wang, Lei; You, Zhu‐Hong; Zhan, Zhao-Hui; Li, Haoyuan

doi:10.1093/bib/bbac393

Cited by 11 publications

(4 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The widely accepted network representation learning model (LINE [ 46 ]) can be considered as learning low-dimensional dense vectors by preserving 1st and 2nd order proximity using a multi-layer perceptron. For objective functions of 1st and 2nd orders, respectively, are shown below:…”

Section: Methodsmentioning

confidence: 99%

Biolinguistic graph fusion model for circRNA–miRNA association prediction

Guo,

Wang,

You

et al. 2024

Briefings in Bioinformatics

Self Cite

View full text Add to dashboard Cite

Emerging clinical evidence suggests that sophisticated associations with circular ribonucleic acids (RNAs) (circRNAs) and microRNAs (miRNAs) are a critical regulatory factor of various pathological processes and play a critical role in most intricate human diseases. Nonetheless, the above correlations via wet experiments are error-prone and labor-intensive, and the underlying novel circRNA–miRNA association (CMA) has been validated by numerous existing computational methods that rely only on single correlation data. Considering the inadequacy of existing machine learning models, we propose a new model named BGF-CMAP, which combines the gradient boosting decision tree with natural language processing and graph embedding methods to infer associations between circRNAs and miRNAs. Specifically, BGF-CMAP extracts sequence attribute features and interaction behavior features by Word2vec and two homogeneous graph embedding algorithms, large-scale information network embedding and graph factorization, respectively. Multitudinous comprehensive experimental analysis revealed that BGF-CMAP successfully predicted the complex relationship between circRNAs and miRNAs with an accuracy of 82.90% and an area under receiver operating characteristic of 0.9075. Furthermore, 23 of the top 30 miRNA-associated circRNAs of the studies on data were confirmed in relevant experiences, showing that the BGF-CMAP model is superior to others. BGF-CMAP can serve as a helpful model to provide a scientific theoretical basis for the study of CMA prediction.

show abstract

Section: Methodsmentioning

confidence: 99%

Biolinguistic graph fusion model for circRNA–miRNA association prediction

Guo,

Wang,

You

et al. 2024

Briefings in Bioinformatics

Self Cite

View full text Add to dashboard Cite

show abstract

“…MCMKL-PDA is contrasted with four cutting-edge meth-ods iPiDA_GCN [23], iPiDA_SWGCN [24], GAPDA [33], and MSRDA [22]. In this study, for a fair comparison, we employed either default parameter configurations or the optimal configurations determined through grid search in the compared approaches.…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

A matrix completion-based multikernel learning approach for predicting the association between piRNA and diseases

Shi,

Yue,

Wang

2024

Third International Conference on Biomedical and Intelligent Systems (IC-BIS 2024)

View full text Add to dashboard Cite

With advancements in bioinformatics, an accumulating body of evidence highlights the substantial implication of piRNA in the progression of various diseases. piRNAs are acknowledged as potential therapeutic targets and biomarkers for disease diagnosis and therapy. Exploring the intricate relationship between piRNA and diseases through computational methods can streamline costs, improve efficiency, and mitigate experimental risks. However, existing computational methods face challenges related to the sparsity of heterogeneous adjacency matrix and inherent noise in similarity networks. Therefore, in this study, we propose a matrix completion-based multi-kernel learning approach for predicting the association between piRNA and diseases, termed MCMKL-PDA. The primary goal of MCMKL-PDA is to populate the sparse adjacency matrix and mitigate the impact of noise from similarity networks on model performance. Specifically, firstly, compute piRNA and disease similarity networks using data from multiple sources. Subsequently, employ bounded matrix completion (BMC) to prefill piRNA-disease association matrices. Next, employ the low-rank fast kernel learning (LRFKL) algorithm fusion similarity networks, and the Network Enhancement (NE) algorithm refines the fused similarity networks. Finally, the Random Forests (RF) methodology is applied to forecast potential associations. The experimental results unequivocally establish the superior performance of MCMKL-PDA over other state-of-the-art methods. Moreover, independent test sets further substantiate the practical effectiveness of MCMKL-PDA.

show abstract

“…GNNs can efficiently represent the interplay between ncRNA similarity networks and disease similarity networks; this potential has been recognized early on in GNN emergence [90][91][92][93]. Subsequent and recent work in the context of cancer included using GNN models for miRNA-cancer association prediction [94][95][96][97][98][99][100], piRNA-cancer association prediction [101], lncRNA-cancer association prediction [102][103][104][105], and circRNA-cancer association prediction [106]. An interesting recent development is using multimodal GNNs to predict association not with disease but with anticancer drug resistance -for example, Liu et al [107] incorporated disease-related information into the multimodal GNN predictor of circRNA-drug resistance associations, whereas Gao and Shang [108] used a GAT model for identifying lncRNA-drug resistance associations.…”

Section: Prediction Of Ncrna (Mirna Pirna Lncrna) and Circrna-cancer ...mentioning

confidence: 99%

Graph Neural Networks in Cancer and Oncology Research: Emerging and Future Trends

Gogoshin,

Rodin

2023

Preprint

View full text Add to dashboard Cite

Next-generation cancer and oncology research needs to take full advantage of the multi-modal structured, or graph, information, with the graph datatypes ranging from molecular structures to spatially resolved imaging and digital pathology to biological networks to knowledge graphs. Graph Neural Networks (GNNs) efficiently combine the graph structure representations with the high predictive performance of deep learning, especially on the large multi-modal datasets. In this review article, we survey the landscape of recent (2020-present) GNN applications in the context of cancer and oncology research, and delineate six currently predominant research areas. Subsequently, we identify the most promising directions for future research. We compare GNNs with graphical models and "non-structured" deep learning, and devise the guidelines for cancer and oncology researchers or physician-scientists asking the question of whether they should adopt the GNN methodology in their research pipelines.

show abstract

Line graph attention networks for predicting disease-associated Piwi-interacting RNAs

Cited by 11 publications

References 51 publications

Biolinguistic graph fusion model for circRNA–miRNA association prediction

Biolinguistic graph fusion model for circRNA–miRNA association prediction

A matrix completion-based multikernel learning approach for predicting the association between piRNA and diseases

Graph Neural Networks in Cancer and Oncology Research: Emerging and Future Trends

Contact Info

Product

Resources

About