Drug-Disease Association Prediction Based on Neighborhood Information Aggregation in Neural Networks

Abstract Background: LncRNAs (Long non-coding RNAs) are a type of non-coding RNA molecule with transcript length longer than 200 nucleotides. LncRNA has been novel candidate biomarkers in cancer diagnosis and prognosis. However, it is difficult to discover the true association mechanism between lncRNAs and complex diseases. The unprecedented enrichment of multi-omics data and the rapid development of machine learning technology provide us with the opportunity to design a machine learning framework to study the relationship between lncRNAs and complex diseases. Results: In this article, we proposed a new machine learning approach, namely LGDLDA (LncRNA-Gene-Disease association networks based LncRNA-Disease Association prediction), for disease-related lncRNAs association prediction based multi-omics data, machine learning methods and neural network neighborhood information aggregation. Firstly, LGDLDA calculates the similarity matrix of lncRNA, gene and disease respectively. LGDLDA calculates the similarity between lncRNAs through the lncRNA expression profile matrix, lncRNA-miRNA interaction matrix and lncRNA-protein interaction matrix. LGDLDA obtains gene similarity matrix by calculating the lncRNA-gene association matrix and the gene-disease association matrix. LGDLDA obtains disease similarity matrix by calculating the disease ontology, the disease-miRNA association matrix, and Gaussian interaction profile kernel similarity. Secondly, LGDLDA integrates the neighborhood information in similarity matrices by using nonlinear feature learning of neural network. Thirdly, LGDLDA uses embedded node representations to approximate the observed matrices. Finally, LGDLDA ranks candidate lncRNA-disease pairs and then selects potential disease-related lncRNAs. Conclusions: Compared with lncRNA-disease prediction methods, IHI-BMLLR takes into account more critical information and obtains the performance improvement cancer-related lncRNA predictions. Randomly split data experiment results show that the stability of LGDLDA is better than IDHI-MIRW, NCPLDA, LncDisAP and NCPHLDA. The results on different simulation data sets show that LGDLDA can accurately and effectively predict the disease-related lncRNAs. Furthermore, we applied LGDLDA to three real cancer data including gastric cancer, colorectal cancer and breast cancer to predict potential cancer-related lncRNAs.

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Convolution Neural Networks Using Deep Matrix Factorization for Predicting circRNA-Disease Association

IEEE/ACM Transactions on Computational Biology and Bioinformatics ◽

10.1109/tcbb.2021.3138339 ◽

2021 ◽

pp. 1-1

Author(s):

Zhihao Liu ◽

Cunmei Ji ◽

Jian-Cheng Ni ◽

Yu-Tian Wang ◽

Lijuan Qiao ◽

...

Keyword(s):

Neural Networks ◽

Matrix Factorization ◽

Disease Association ◽

Convolution Neural Networks

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EOESGC: predicting miRNA-disease associations based on embedding of embedding and simplified graph convolutional network

BMC Medical Informatics and Decision Making ◽

10.1186/s12911-021-01671-y ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Shanchen Pang ◽

Yu Zhuang ◽

Xinzeng Wang ◽

Fuyu Wang ◽

Sibo Qiao

Keyword(s):

Information Aggregation ◽

Disease Association ◽

Supplementary Information ◽

Graph Structure ◽

Association Network ◽

Convolutional Network ◽

Edge Information ◽

Biological Studies ◽

Convolution Model ◽

Disease Associations

Abstract Background A large number of biological studies have shown that miRNAs are inextricably linked to many complex diseases. Studying the miRNA-disease associations could provide us a root cause understanding of the underlying pathogenesis in which promotes the progress of drug development. However, traditional biological experiments are very time-consuming and costly. Therefore, we come up with an efficient models to solve this challenge. Results In this work, we propose a deep learning model called EOESGC to predict potential miRNA-disease associations based on embedding of embedding and simplified convolutional network. Firstly, integrated disease similarity, integrated miRNA similarity, and miRNA-disease association network are used to construct a coupled heterogeneous graph, and the edges with low similarity are removed to simplify the graph structure and ensure the effectiveness of edges. Secondly, the Embedding of embedding model (EOE) is used to learn edge information in the coupled heterogeneous graph. The training rule of the model is that the associated nodes are close to each other and the unassociated nodes are far away from each other. Based on this rule, edge information learned is added into node embedding as supplementary information to enrich node information. Then, node embedding of EOE model training as a new feature of miRNA and disease, and information aggregation is performed by simplified graph convolution model, in which each level of convolution can aggregate multi-hop neighbor information. In this step, we only use the miRNA-disease association network to further simplify the graph structure, thus reducing the computational complexity. Finally, feature embeddings of both miRNA and disease are spliced into the MLP for prediction. On the EOESGC evaluation part, the AUC, AUPR, and F1-score of our model are 0.9658, 0.8543 and 0.8644 by 5-fold cross-validation respectively. Compared with the latest published models, our model shows better results. In addition, we predict the top 20 potential miRNAs for breast cancer and lung cancer, most of which are validated in the dbDEMC and HMDD3.2 databases. Conclusion The comprehensive experimental results show that EOESGC can effectively identify the potential miRNA-disease associations.

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Knowledge Graph Alignment Network with Gated Multi-Hop Neighborhood Aggregation

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i01.5354 ◽

2020 ◽

Vol 34 (01) ◽

pp. 222-229

Author(s):

Zequn Sun ◽

Chengming Wang ◽

Wei Hu ◽

Muhao Chen ◽

Jian Dai ◽

...

Keyword(s):

Neural Networks ◽

Attention Mechanism ◽

Knowledge Graph ◽

Neighborhood Information ◽

Graph Alignment ◽

Gating Mechanism ◽

Neighborhood Structures ◽

Knowledge Graphs ◽

End To End ◽

Graph Neural Networks

Graph neural networks (GNNs) have emerged as a powerful paradigm for embedding-based entity alignment due to their capability of identifying isomorphic subgraphs. However, in real knowledge graphs (KGs), the counterpart entities usually have non-isomorphic neighborhood structures, which easily causes GNNs to yield different representations for them. To tackle this problem, we propose a new KG alignment network, namely AliNet, aiming at mitigating the non-isomorphism of neighborhood structures in an end-to-end manner. As the direct neighbors of counterpart entities are usually dissimilar due to the schema heterogeneity, AliNet introduces distant neighbors to expand the overlap between their neighborhood structures. It employs an attention mechanism to highlight helpful distant neighbors and reduce noises. Then, it controls the aggregation of both direct and distant neighborhood information using a gating mechanism. We further propose a relation loss to refine entity representations. We perform thorough experiments with detailed ablation studies and analyses on five entity alignment datasets, demonstrating the effectiveness of AliNet.

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Neural networks and disease association studies

American Journal of Medical Genetics ◽

10.1002/1096-8628(20010108)105:1<60::aid-ajmg1062>3.0.co;2-l ◽

2001 ◽

Vol 105 (1) ◽

pp. 60-61 ◽

Cited By ~ 17

Author(s):

Jurg Ott

Keyword(s):

Neural Networks ◽

Association Studies ◽

Disease Association

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Analysis of disease association and susceptibility for SNP data using emotional neural networks

2014 International Joint Conference on Neural Networks (IJCNN) ◽

10.1109/ijcnn.2014.6889423 ◽

2014 ◽

Cited By ~ 1

Author(s):

Xiao Wang ◽

Qinke Peng ◽

Tao Zhong

Keyword(s):

Neural Networks ◽

Disease Association ◽

Snp Data

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Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networks

ACM Transactions on Information Systems ◽

10.1145/3490181 ◽

2022 ◽

Vol 40 (4) ◽

pp. 1-46

Author(s):

Hao Peng ◽

Ruitong Zhang ◽

Yingtong Dou ◽

Renyu Yang ◽

Jingyi Zhang ◽

...

Keyword(s):

Neural Network ◽

Neural Networks ◽

Network Architecture ◽

Message Passing ◽

Representation Learning ◽

Graph Representations ◽

Graph Data ◽

Neighborhood Information ◽

Neighborhood Selection ◽

Graph Neural Networks

Graph Neural Networks (GNNs) have been widely used for the representation learning of various structured graph data, typically through message passing among nodes by aggregating their neighborhood information via different operations. While promising, most existing GNNs oversimplify the complexity and diversity of the edges in the graph and thus are inefficient to cope with ubiquitous heterogeneous graphs, which are typically in the form of multi-relational graph representations. In this article, we propose RioGNN , a novel Reinforced, recursive, and flexible neighborhood selection guided multi-relational Graph Neural Network architecture, to navigate complexity of neural network structures whilst maintaining relation-dependent representations. We first construct a multi-relational graph, according to the practical task, to reflect the heterogeneity of nodes, edges, attributes, and labels. To avoid the embedding over-assimilation among different types of nodes, we employ a label-aware neural similarity measure to ascertain the most similar neighbors based on node attributes. A reinforced relation-aware neighbor selection mechanism is developed to choose the most similar neighbors of a targeting node within a relation before aggregating all neighborhood information from different relations to obtain the eventual node embedding. Particularly, to improve the efficiency of neighbor selecting, we propose a new recursive and scalable reinforcement learning framework with estimable depth and width for different scales of multi-relational graphs. RioGNN can learn more discriminative node embedding with enhanced explainability due to the recognition of individual importance of each relation via the filtering threshold mechanism. Comprehensive experiments on real-world graph data and practical tasks demonstrate the advancements of effectiveness, efficiency, and the model explainability, as opposed to other comparative GNN models.

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