gGATLDA: lncRNA-disease association prediction based on graph-level graph attention network

Abstract

Background Long non-coding RNAs (lncRNAs) are related to human diseases by regulating gene expression. Identifying lncRNA-disease associations (LDAs) will contribute to diagnose, treatment, and prognosis of diseases. However, the identification of LDAs by the biological experiments is time-consuming, costly and inefficient. Therefore, the development of efficient and high-accuracy computational methods for predicting LDAs is of great significance. Results In this paper, we propose a novel computational method (gGATLDA) to predict LDAs based on graph-level graph attention network. Firstly, we extract the enclosing subgraphs of each lncRNA-disease pair. Secondly, we construct the feature vectors by integrating lncRNA similarity and disease similarity as node attributes in subgraphs. Finally, we train a graph neural network (GNN) model by feeding the subgraphs and feature vectors to it, and use the trained GNN model to predict lncRNA-disease potential association scores. The experimental results show that our method can achieve higher area under the receiver operation characteristic curve (AUC), area under the precision recall curve (AUPR), accuracy and F1-Score than the state-of-the-art methods in five fold cross-validation. Case studies show that our method can effectively identify lncRNAs associated with breast cancer, gastric cancer, prostate cancer, and renal cancer. Conclusion The experimental results indicate that our method is a useful approach for predicting potential LDAs.

SEBGLMA: Semantic Embedded Bipartite Graph Network for Predicting lncRNA-miRNA Associations

“…where true positive (TP) represents the sum of associated lncRNA-miRNA pairs which are detected as positive samples; true negative (TN) records the number of nonassociated pairs which are classifed as negative samples; false positive (FP) denotes the aggregate of associated lncRNA-miRNA pairs which are inferred as negative samples; false negative (FN) is the count of nonassociated pairs which are predicted as positive samples. In addition, Receiver Operating Characteristic (ROC) and Precision-Recall (PR) curves are depicted to visualize the experimental results [39,40]. Te area under the curves (AUC) and area under the PR (AUPR) values are also attached to ROC and PR curves for justifying our model and indicating the sample balance.…”

Gra-CRC-miRTar: The pre-trained nucleotide-to-graph neural networks to identify potential miRNA targets in colorectal cancer

“…Moreover, the advent of graph neural networks (GNNs) [34][35][36] has recently gained significant attention and been applied to a variety of bioinformatic problems such as protein-protein interaction 37,38 , RNA-disease association identification 39,40 , RNA subcellular localization prediction 41,42 , as well as RNA-RNA association prediction 43,44 . Since miRNA-induced silencing complex (miRISC) molecules directly attach to the targeted RNAs, creating intricate graph-like and spatial secondary structures, GNNs present great potential to identify RNA-RNA associations in an end-to-end manner through graph representation of the duplex that can better learn complex interactions between RNAs in a regulatory network.…”

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

“…The VADLP model [ 33 ] constructed multilayer graphs to integrate multiple similarities and employed variance autoencoders and CNN for lncRNA-disease interaction inference. The gGATLDA model [ 34 ] utilized attention mechanisms at the graph level. During the graph construction process, each disease-lncRNA pair is extracted to form a subgraph for lncRNA-disease relationship calculation.…”