Data resources and computational methods for lncRNA-disease association prediction

Sheng, Nan; Huang, Lan; Lu, Yuting; Wang, Hao; Yang, Lili; Gao, Ling; Xie, Xuping; Fu, Yuan; Wang, Yan

doi:10.1016/j.compbiomed.2022.106527

Cited by 23 publications

(6 citation statements)

References 148 publications

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“…As shown in Figure 5(a), different layer numbers affect the model's AUC, while Figure 5(b) illustrates the impact of different values of L1 and L2 on AUPR. The highest AUC value is achieved when the combination of (L1, L2) is set to (10,20), while the highest AUPR value is achieved when it is set to (15,10). Additionally, different combinations of the quantity for the attention heads, Head1 and Head2, also affect the prediction efficiency of the model.…”

Section: Parameter Analysismentioning

confidence: 99%

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Node-adaptive graph Transformer with structural encoding for accurate and robust lncRNA-disease association prediction

Li,

Bai,

Liang

et al. 2023

Preprint

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Background Long noncoding RNAs (lncRNAs) are integral to a plethora of critical cellular biological processes, including the regulation of gene expression, cell differentiation, and the development of tumors and cancers. Predicting the relationships between lncRNAs and diseases can contribute to a better understanding of the pathogenic mechanisms of disease and provide strong support for the development of advanced treatment methods.Results Therefore, we present an innovative node-adaptive Transformer model for predicting unknown associations between lncRNAs and diseases (GNATLDA). First, we utilize the node-adaptive feature smoothing (NAFS) method to learn the local feature information of nodes and encode the structural information of the fusion similarity network of diseases and lncRNAs using Structural Deep Network Embedding (SDNE). Next, the Transformer module, which contains a multi-headed attention layer, is used to learn global feature information about the nodes of the heterogeneous network, which is used to capture potential association information between the network nodes. Finally, we employ a Transformer module with two multi-headed attention layers for learning global-level embedding fusion. Network structure coding is added as the structural inductive bias of the network to compensate for the missing message-passing mechanism in Transformer. Our model accounts for both local-level and global-level node information and exploits the global horizon of the Transformer model, which fuses the structural inductive bias of the network to comprehensively investigate unidentified associations between nodes, significantly increasing the predictive effectiveness of potential interactions between diseases and lncRNAs. We conducted case studies on four diseases; 55 out of 60 interactions between diseases and lncRNAs were confirmed by the literature.Conclusions Our proposed GNATLDA model can serve as a highly efficient computational method for predicting biological information associations.

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Section: Parameter Analysismentioning

confidence: 99%

“…Numerous computational techniques for exploring disease-lncRNA interactions have emerged with the continual advancement of diverse technology. We can classify the available computational methods into bioinformatics network-based methods [15] and deep learning-based methods [16].…”

Section: Introductionmentioning

confidence: 99%

Node-adaptive graph Transformer with structural encoding for accurate and robust lncRNA-disease association prediction

Li,

Bai,

Liang

et al. 2023

Preprint

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“…Previous studies have identified a large number of non-coding RNAs (ncRNAs) in the mammalian genome, and while it is entirely possible that most of these ncRNAs are transcriptional noise or by-products of RNA processing, there is growing evidence that most of them are functional and provide a variety of regulatory activities in the cell [1]. Recent research underscores the intimate association between ncRNAs and the onset and progression of specific diseases [2]. Particularly, most ncRNAs exhibit varying local concentrations, interacting partners, post-transcriptional modifications, and regulatory pathways in diverse subcellular locations.…”

Section: Introductionmentioning

confidence: 99%

GP-HTNLoc: A Graph Prototype Head-Tail Network-based Model for Multi-label Subcellular Localization Prediction of ncRNAs

Han,

Liu

2024

Preprint

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Numerous research findings demonstrated that understanding the subcellular localization of non-coding RNAs (ncRNAs) is pivotal in elucidating their roles and regulatory mechanisms in cells. Despite the existence of over ten computational models dedicated to predicting the subcellular localization of ncRNAs, a majority of these models are designed solely for single-label prediction. In reality, ncRNAs often exhibit localization across multiple subcellular compartments. Furthermore, the existing multi-label localization prediction models are insufficient in addressing the challenges posed by the scarcity of training samples and class imbalance in ncRNA dataset. This study addresses the limitations of existing models by introducing a novel multi-label localization prediction model for ncRNAs, termed GP-HTNLoc. To alleviate class imbalance, the model adopts a separate training approach for head and tail class labels. In GP-HTNLoc, a pioneering graph prototype module is introduced for capturing potential association of ncRNA samples with labels. This module efficiently learns the graph structure and aggregates sample features. Notably, only few samples are required to obtain label prototypes containing rich information. These prototypes are then utilized to train a transfer learner, facilitating the transfer of meta-knowledge from the head class to the tail class. Experimental results demonstrate that GP-HTNLoc surpasses current state-of-the-art models across all datasets. Ablation study underscore the vital role played by the graph prototype module in enhancing the performance of GP-HTNLoc. The user-friendly online GP-HTNLoc web server can be accessed at https://56s8y85390.goho.co.

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Section: Introductionmentioning

confidence: 99%

Node-adaptive graph Transformer with structural encoding for accurate and robust lncRNA-disease association prediction

Li,

Bai,

Liang

et al. 2024

BMC Genomics

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Background Long noncoding RNAs (lncRNAs) are integral to a plethora of critical cellular biological processes, including the regulation of gene expression, cell differentiation, and the development of tumors and cancers. Predicting the relationships between lncRNAs and diseases can contribute to a better understanding of the pathogenic mechanisms of disease and provide strong support for the development of advanced treatment methods. Results Therefore, we present an innovative Node-Adaptive Graph Transformer model for predicting unknown LncRNA-Disease Associations, named NAGTLDA. First, we utilize the node-adaptive feature smoothing (NAFS) method to learn the local feature information of nodes and encode the structural information of the fusion similarity network of diseases and lncRNAs using Structural Deep Network Embedding (SDNE). Next, the Transformer module is used to capture potential association information between the network nodes. Finally, we employ a Transformer module with two multi-headed attention layers for learning global-level embedding fusion. Network structure coding is added as the structural inductive bias of the network to compensate for the missing message-passing mechanism in Transformer. NAGTLDA achieved an average AUC of 0.9531 and AUPR of 0.9537 significantly higher than state-of-the-art methods in 5-fold cross validation. We perform case studies on 4 diseases; 55 out of 60 associations between lncRNAs and diseases have been validated in the literatures. The results demonstrate the enormous potential of the graph Transformer structure to incorporate graph structural information for uncovering lncRNA-disease unknown correlations. Conclusions Our proposed NAGTLDA model can serve as a highly efficient computational method for predicting biological information associations.

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Data resources and computational methods for lncRNA-disease association prediction

Cited by 23 publications

References 148 publications

Node-adaptive graph Transformer with structural encoding for accurate and robust lncRNA-disease association prediction

Node-adaptive graph Transformer with structural encoding for accurate and robust lncRNA-disease association prediction

GP-HTNLoc: A Graph Prototype Head-Tail Network-based Model for Multi-label Subcellular Localization Prediction of ncRNAs

Node-adaptive graph Transformer with structural encoding for accurate and robust lncRNA-disease association prediction

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