Predicting gene-disease associations via graph embedding and graph convolutional networks

2019 ◽  
Author(s):  
Lvxing Zhu ◽  
Zhaolin Hong ◽  
Haoran Zheng
Keyword(s):  
Graph Embedding ◽  
Convolutional Networks ◽  
Disease Associations

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

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.

Recalibration convolutional networks for learning interaction knowledge graph embedding

2021 ◽  
Vol 427 ◽  
pp. 118-130
Author(s):  
Zhifei Li ◽  
Hai Liu ◽  
Zhaoli Zhang ◽  
Tingting Liu ◽  
Jiangbo Shu
Keyword(s):  
Graph Embedding ◽  
Knowledge Graph ◽  
Convolutional Networks

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

2020 ◽  
Vol 36 (8) ◽  
pp. 2538-2546 ◽  
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.

scholarly journals Fully Convolutional Networks and a Manifold Graph Embedding-Based Algorithm for PolSAR Image Classification

2020 ◽  
Vol 12 (9) ◽  
pp. 1467 ◽  
Author(s):  
Chu He ◽  
Bokun He ◽  
Mingxia Tu ◽  
Yan Wang ◽  
Tao Qu ◽  
...  
Keyword(s):  
Image Classification ◽  
Rapid Development ◽  
Graph Embedding ◽  
Compact Representation ◽  
Support Vector ◽  
Feature Maps ◽  
Extraction Technology ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Spatial Features

With the rapid development of artificial intelligence, how to take advantage of deep learning and big data to classify polarimetric synthetic aperture radar (PolSAR) imagery is a hot topic in the field of remote sensing. As a key step for PolSAR image classification, feature extraction technology based on target decomposition is relatively mature, and how to extract discriminative spatial features and integrate these features with polarized information to maximize the classification accuracy is the core issue. In this context, this paper proposes a PolSAR image classification algorithm based on fully convolutional networks (FCNs) and a manifold graph embedding model. First, to describe different types of land objects more comprehensively, various polarized features of PolSAR images are extracted through seven kinds of traditional decomposition methods. Afterwards, drawing on transfer learning, the decomposed features are fed into multiple parallel and pre-trained FCN-8s models to learn deep multi-scale spatial features. Feature maps from the last layer of each FCN model are concatenated to obtain spatial polarization features with high dimensions. Then, a manifold graph embedding model is adopted to seek an effective and compact representation for spatially polarized features in a manifold subspace, simultaneously removing redundant information. Finally, a support vector machine (SVM) is selected as the classifier for pixel-level classification in a manifold subspace. Extensive experiments on three PolSAR datasets demonstrate that the proposed algorithm achieves a superior classification performance.

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

Cells ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 977 ◽  
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.

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