IGNSCDA: Predicting CircRNA-Disease Associations Based on Improved Graph Convolutional Network and Negative Sampling

Aberrant expressions of long non-coding RNAs (lncRNAs) are often associated with diseases and identification of disease-related lncRNAs is helpful for elucidating complex pathogenesis. Recent methods for predicting associations between lncRNAs and diseases integrate their pertinent heterogeneous data. However, they failed to deeply integrate topological information of heterogeneous network comprising lncRNAs, diseases, and miRNAs. We proposed a novel method based on the graph convolutional network and convolutional neural network, referred to as GCNLDA, to infer disease-related lncRNA candidates. The heterogeneous network containing the lncRNA, disease, and miRNA nodes, is constructed firstly. The embedding matrix of a lncRNA-disease node pair was constructed according to various biological premises about lncRNAs, diseases, and miRNAs. A new framework based on a graph convolutional network and a convolutional neural network was developed to learn network and local representations of the lncRNA-disease pair. On the left side of the framework, the autoencoder based on graph convolution deeply integrated topological information within the heterogeneous lncRNA-disease-miRNA network. Moreover, as different node features have discriminative contributions to the association prediction, an attention mechanism at node feature level is constructed. The left side learnt the network representation of the lncRNA-disease pair. The convolutional neural networks on the right side of the framework learnt the local representation of the lncRNA-disease pair by focusing on the similarities, associations, and interactions that are only related to the pair. Compared to several state-of-the-art prediction methods, GCNLDA had superior performance. Case studies on stomach cancer, osteosarcoma, and lung cancer confirmed that GCNLDA effectively discovers the potential lncRNA-disease associations.

Download Full-text

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.

Download Full-text

Inferring disease-associated microRNAs using semi-supervised multi-label graph convolutional networks

10.1101/666719 ◽

2019 ◽

Author(s):

Xiaoyong Pan ◽

Hong-Bin Shen

Keyword(s):

Expression Profiles ◽

Tissue Expression ◽

Computational Method ◽

Interaction Networks ◽

Convolutional Network ◽

Protein Coding ◽

Convolutional Networks ◽

Disease Associations ◽

Supervised Methods ◽

Inflammatory Bowel

AbstractMicroRNAs (miRNAs) play crucial roles in many biological processes involved in diseases. The associations between diseases and protein coding genes (PCGs) have been well investigated, and further the miRNAs interact with PCGs to trigger them to be functional. Thus, it is imperative to computationally infer disease-miRNA associations under the context of interaction networks.In this study, we present a computational method, DimiG, to infer miRNA-associated diseases using semi-supervised Graph Convolutional Network model (GCN). DimiG is a multi-label framework to integrate PCG-PCG interactions, PCG-miRNA interactions, PCG-disease associations and tissue expression profiles. DimiG is trained on disease-PCG associations and a graph constructed from interaction networks of PCG-PCG and miRNA-PCG using semi-supervised GCN, which is further used to score associations between diseases and miRNAs. We evaluate DimiG on a benchmark set collected from verified disease-miRNA associations. Our results demonstrate that the new DimiG yields promising performance and outperforms the best published baseline method not trained on disease-miRNA associations by 11% and is also superior to two state-of-the-art supervised methods trained on disease-miRNA associations. Three case studies of prostate cancer, lung cancer and Inflammatory bowel disease further demonstrate the efficacy of DimiG, where the top miRNAs predicted by DimiG for them are supported by literature or databases.

Download Full-text

EOESGC: Predicting miRNA−disease Associations Based on Embedding of Embedding and Simplified Graph Convolutional Network

10.21203/rs.3.rs-831662/v1 ◽

2021 ◽

Author(s):

Shanchen Pang ◽

yu Zhuang ◽

Xinzeng Wang ◽

Fuyu Wang ◽

Sibo Qiao

Keyword(s):

Positional Information ◽

Disease Association ◽

Feature Representation ◽

Experimental Results ◽

Graph Structure ◽

Convolutional Network ◽

Structure Information ◽

Biological Studies ◽

Disease Associations ◽

Computational Difficulty

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 on 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 more 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, a coupled heterogeneous graph is constructed by using the integrated disease similarity, integrated miRNA similarity and miRNA−disease association networks where parts of the connected edges with less similarity values are removed to simplify the graph structure. The initial feature representation of nodes in the graph is learned using the embedding of embedding model(EOE) based on the principle that the nodes with associations are close to each other and the nodes without association are far from each other. The use of EOE can effectively learn the positional information among nodes and protect the graph structure information to some extent. Then the initial features of the nodes are fed into the simplified graph convolutional network(SGC), and in this step we only use miRNA−disease association network to further simplify the graph structure and thus reduce the computational complexity. Finally, feature embeddings of both miRNA and disease spliced into the MLP for prediction. The two graph simplifications of our model effectively reduce the computational difficulty, and the experimental results show that our model can indeed predict the potential miRNA−disease associations effectively. Compared with the latest published models, our model shows better results. On EOESGC evaluation part, the AUC, AUPR and F1 of our model are 0.9658, 0.8543 and 0.8644 by 5−fold cross validation respectively. 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.

Download Full-text

GCNSDA: Predicting snoRNA-disease associations via graph convolutional network

10.1109/bibm52615.2021.9669505 ◽

2021 ◽

Author(s):

Dayun Liu ◽

Yi Luo ◽

Jingjing Zheng ◽

Hanlin Xu ◽

Jiaxuan Zhang ◽

...

Keyword(s):

Convolutional Network ◽

Disease Associations

Download Full-text

GCNCDA: A new method for predicting circRNA-disease associations based on Graph Convolutional Network Algorithm

PLoS Computational Biology ◽

10.1371/journal.pcbi.1007568 ◽

2020 ◽

Vol 16 (5) ◽

pp. e1007568 ◽

Cited By ~ 8

Author(s):

Lei Wang ◽

Zhu-Hong You ◽

Yang-Ming Li ◽

Kai Zheng ◽

Yu-An Huang

Keyword(s):

New Method ◽

Convolutional Network ◽

Network Algorithm ◽

Disease Associations

Download Full-text

NMCMDA: neural multicategory MiRNA–disease association prediction

Briefings in Bioinformatics ◽

10.1093/bib/bbab074 ◽

2021 ◽

Author(s):

Jingru Wang ◽

Jin Li ◽

Kun Yue ◽

Li Wang ◽

Yuyun Ma ◽

...

Keyword(s):

Cost Effective ◽

Underlying Mechanism ◽

Disease Association ◽

Disease Category ◽

Convolutional Network ◽

Time Saving ◽

Disease Associations ◽

Multiple Category ◽

Latent Representations ◽

Main Components

Abstract Motivation There is growing evidence showing that the dysregulations of miRNAs cause diseases through various kinds of the underlying mechanism. Thus, predicting the multiple-category associations between microRNAs (miRNAs) and diseases plays an important role in investigating the roles of miRNAs in diseases. Moreover, in contrast with traditional biological experiments which are time-consuming and expensive, computational approaches for the prediction of multicategory miRNA–disease associations are time-saving and cost-effective that are highly desired for us. Results We present a novel data-driven end-to-end learning-based method of neural multiple-category miRNA–disease association prediction (NMCMDA) for predicting multiple-category miRNA–disease associations. The NMCMDA has two main components: (i) encoder operates directly on the miRNA–disease heterogeneous network and leverages Graph Neural Network to learn miRNA and disease latent representations, respectively. (ii) Decoder yields miRNA–disease association scores with the learned latent representations as input. Various kinds of encoders and decoders are proposed for NMCMDA. Finally, the NMCMDA with the encoder of Relational Graph Convolutional Network and the neural multirelational decoder (NMR-RGCN) achieves the best prediction performance. We compared the NMCMDA with other baselines on three experimental datasets. The experimental results show that the NMR-RGCN is significantly superior to the state-of-the-art method TDRC in terms of Top-1 precision, Top-1 Recall, and Top-1 F1. Additionally, case studies are provided for two high-risk human diseases (namely, breast cancer and lung cancer) and we also provide the prediction and validation of top-10 miRNA–disease-category associations based on all known data of HMDD v3.2, which further validate the effectiveness and feasibility of the proposed method.

Download Full-text

Neural inductive matrix completion with graph convolutional networks for miRNA-disease association prediction

Bioinformatics ◽

10.1093/bioinformatics/btz965 ◽

2020 ◽

Vol 36 (8) ◽

pp. 2538-2546 ◽

Cited By ~ 9

Author(s):

Jin Li ◽

Sai Zhang ◽

Tao Liu ◽

Chenxi Ning ◽

Zhuoxuan Zhang ◽

...

Keyword(s):

Characteristic Curve ◽

Matrix Completion ◽

Disease Association ◽

Supplementary Information ◽

Convolutional Network ◽

Convolutional Networks ◽

Feature Representations ◽

Disease Similarity ◽

Disease Associations ◽

Association Data

Abstract Motivation Predicting the association between microRNAs (miRNAs) and diseases plays an import role in identifying human disease-related miRNAs. As identification of miRNA-disease associations via biological experiments is time-consuming and expensive, computational methods are currently used as effective complements to determine the potential associations between disease and miRNA. Results We present a novel method of neural inductive matrix completion with graph convolutional network (NIMCGCN) for predicting miRNA-disease association. NIMCGCN first uses graph convolutional networks to learn miRNA and disease latent feature representations from the miRNA and disease similarity networks. Then, learned features were input into a novel neural inductive matrix completion (NIMC) model to generate an association matrix completion. The parameters of NIMCGCN were learned based on the known miRNA-disease association data in a supervised end-to-end way. We compared the proposed method with other state-of-the-art methods. The area under the receiver operating characteristic curve results showed that our method is significantly superior to existing methods. Furthermore, 50, 47 and 48 of the top 50 predicted miRNAs for three high-risk human diseases, namely, colon cancer, lymphoma and kidney cancer, were verified using experimental literature. Finally, 100% prediction accuracy was achieved when breast cancer was used as a case study to evaluate the ability of NIMCGCN for predicting a new disease without any known related miRNAs. Availability and implementation https://github.com/ljatynu/NIMCGCN/ Supplementary information Supplementary data are available at Bioinformatics online.

Download Full-text

Predicting drug–disease associations through layer attention graph convolutional network

Briefings in Bioinformatics ◽

10.1093/bib/bbaa243 ◽

2020 ◽

Author(s):

Zhouxin Yu ◽

Feng Huang ◽

Xiaohan Zhao ◽

Wenjie Xiao ◽

Wen Zhang

Keyword(s):

Characteristic Curve ◽

Attention Mechanism ◽

Computational Method ◽

Convolutional Network ◽

Disease Associations ◽

Precision Recall Curve ◽

Multiple Graph ◽

Novel Associations ◽

Better Than

Abstract Background: Determining drug–disease associations is an integral part in the process of drug development. However, the identification of drug–disease associations through wet experiments is costly and inefficient. Hence, the development of efficient and high-accuracy computational methods for predicting drug–disease associations is of great significance. Results: In this paper, we propose a novel computational method named as layer attention graph convolutional network (LAGCN) for the drug–disease association prediction. Specifically, LAGCN first integrates the known drug–disease associations, drug–drug similarities and disease–disease similarities into a heterogeneous network, and applies the graph convolution operation to the network to learn the embeddings of drugs and diseases. Second, LAGCN combines the embeddings from multiple graph convolution layers using an attention mechanism. Third, the unobserved drug–disease associations are scored based on the integrated embeddings. Evaluated by 5-fold cross-validations, LAGCN achieves an area under the precision–recall curve of 0.3168 and an area under the receiver–operating characteristic curve of 0.8750, which are better than the results of existing state-of-the-art prediction methods and baseline methods. The case study shows that LAGCN can discover novel associations that are not curated in our dataset. Conclusion: LAGCN is a useful tool for predicting drug–disease associations. This study reveals that embeddings from different convolution layers can reflect the proximities of different orders, and combining the embeddings by the attention mechanism can improve the prediction performances.

Download Full-text

A Novel Computational Model for Predicting microRNA–Disease Associations Based on Heterogeneous Graph Convolutional Networks

Cells ◽

10.3390/cells8090977 ◽

2019 ◽

Vol 8 (9) ◽

pp. 977 ◽

Cited By ~ 6

Author(s):

Li ◽

Liu ◽

Hu ◽

Que ◽

Yao

Keyword(s):

Computational Model ◽

Biological Networks ◽

Cross Validation ◽

Mirna Gene ◽

Convolutional Network ◽

Protein Protein Interaction ◽

Convolutional Networks ◽

Disease Associations ◽

Reliable Performance ◽

Leave One Out

Identifying the interactions between disease and microRNA (miRNA) can accelerate drugs development, individualized diagnosis, and treatment for various human diseases. However, experimental methods are time-consuming and costly. So computational approaches to predict latent miRNA–disease interactions are eliciting increased attention. But most previous studies have mainly focused on designing complicated similarity-based methods to predict latent interactions between miRNAs and diseases. In this study, we propose a novel computational model, termed heterogeneous graph convolutional network for miRNA–disease associations (HGCNMDA), which is based on known human protein–protein interaction (PPI) and integrates four biological networks: miRNA–disease, miRNA–gene, disease–gene, and PPI network. HGCNMDA achieved reliable performance using leave-one-out cross-validation (LOOCV). HGCNMDA is then compared to three state-of-the-art algorithms based on five-fold cross-validation. HGCNMDA achieves an AUC of 0.9626 and an average precision of 0.9660, respectively, which is ahead of other competitive algorithms. We further analyze the top-10 unknown interactions between miRNA and disease. In summary, HGCNMDA is a useful computational model for predicting miRNA–disease interactions.

Download Full-text