Predicting drug–disease associations through layer attention graph convolutional network

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.

scholarly journals MGRL: Predicting Drug-Disease Associations Based on Multi-Graph Representation Learning

2021 ◽  
Vol 12 ◽  
Author(s):  
Bo-Wei Zhao ◽  
Zhu-Hong You ◽  
Leon Wong ◽  
Ping Zhang ◽  
Hao-Yuan Li ◽  
...  
Keyword(s):  
High Efficiency ◽  
Drug Repositioning ◽  
Representation Learning ◽  
Computational Method ◽  
Graph Representation ◽  
Practical Applications ◽  
Study Results ◽  
Disease Associations ◽  
Better Than

Drug repositioning is an application-based solution based on mining existing drugs to find new targets, quickly discovering new drug-disease associations, and reducing the risk of drug discovery in traditional medicine and biology. Therefore, it is of great significance to design a computational model with high efficiency and accuracy. In this paper, we propose a novel computational method MGRL to predict drug-disease associations based on multi-graph representation learning. More specifically, MGRL first uses the graph convolution network to learn the graph representation of drugs and diseases from their self-attributes. Then, the graph embedding algorithm is used to represent the relationships between drugs and diseases. Finally, the two kinds of graph representation learning features were put into the random forest classifier for training. To the best of our knowledge, this is the first work to construct a multi-graph to extract the characteristics of drugs and diseases to predict drug-disease associations. The experiments show that the MGRL can achieve a higher AUC of 0.8506 based on five-fold cross-validation, which is significantly better than other existing methods. Case study results show the reliability of the proposed method, which is of great significance for practical applications.

scholarly journals Seq-SymRF: a random forest model predicts potential miRNA-disease associations based on information of sequences and clinical symptoms

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Jinlong Li ◽  
Xingyu Chen ◽  
Qixing Huang ◽  
Yang Wang ◽  
Yun Xie ◽  
...  
Keyword(s):  
Random Forest ◽  
Breast Neoplasms ◽  
Clinical Symptoms ◽  
Characteristic Curve ◽  
Vital Role ◽  
Sequence Information ◽  
Source Codes ◽  
Drug Identification ◽  
Disease Associations ◽  
Precision Recall Curve

Abstract Increasing evidence indicates that miRNAs play a vital role in biological processes and are closely related to various human diseases. Research on miRNA-disease associations is helpful not only for disease prevention, diagnosis and treatment, but also for new drug identification and lead compound discovery. A novel sequence- and symptom-based random forest algorithm model (Seq-SymRF) was developed to identify potential associations between miRNA and disease. Features derived from sequence information and clinical symptoms were utilized to characterize miRNA and disease, respectively. Moreover, the clustering method by calculating the Euclidean distance was adopted to construct reliable negative samples. Based on the fivefold cross-validation, Seq-SymRF achieved the accuracy of 98.00%, specificity of 99.43%, sensitivity of 96.58%, precision of 99.40% and Matthews correlation coefficient of 0.9604, respectively. The areas under the receiver operating characteristic curve and precision recall curve were 0.9967 and 0.9975, respectively. Additionally, case studies were implemented with leukemia, breast neoplasms and hsa-mir-21. Most of the top-25 predicted disease-related miRNAs (19/25 for leukemia; 20/25 for breast neoplasms) and 15 of top-25 predicted miRNA-related diseases were verified by literature and dbDEMC database. It is anticipated that Seq-SymRF could be regarded as a powerful high-throughput virtual screening tool for drug research and development. All source codes can be downloaded from https://github.com/LeeKamlong/Seq-SymRF.

scholarly journals NEDD: a network embedding based method for predicting drug-disease associations

2020 ◽  
Vol 21 (S13) ◽  
Author(s):  
Renyi Zhou ◽  
Zhangli Lu ◽  
Huimin Luo ◽  
Ju Xiang ◽  
Min Zeng ◽  
...  
Keyword(s):  
Drug Repositioning ◽  
Computational Method ◽  
Heterogeneous Information ◽  
Gold Standard Dataset ◽  
Disease Similarity ◽  
Disease Associations ◽  
Meta Path ◽  
Approved Drugs ◽  
Low Dimensional ◽  
Novel Associations

Abstract Background Drug discovery is known for the large amount of money and time it consumes and the high risk it takes. Drug repositioning has, therefore, become a popular approach to save time and cost by finding novel indications for approved drugs. In order to distinguish these novel indications accurately in a great many of latent associations between drugs and diseases, it is necessary to exploit abundant heterogeneous information about drugs and diseases. Results In this article, we propose a meta-path-based computational method called NEDD to predict novel associations between drugs and diseases using heterogeneous information. First, we construct a heterogeneous network as an undirected graph by integrating drug-drug similarity, disease-disease similarity, and known drug-disease associations. NEDD uses meta paths of different lengths to explicitly capture the indirect relationships, or high order proximity, within drugs and diseases, by which the low dimensional representation vectors of drugs and diseases are obtained. NEDD then uses a random forest classifier to predict novel associations between drugs and diseases. Conclusions The experiments on a gold standard dataset which contains 1933 validated drug–disease associations show that NEDD produces superior prediction results compared with the state-of-the-art approaches.

scholarly journals IoT information theft prediction using ensemble feature selection

2022 ◽  
Vol 9 (1) ◽  
Author(s):  
Joffrey L. Leevy ◽  
John Hancock ◽  
Taghi M. Khoshgoftaar ◽  
Jared M. Peterson
Keyword(s):  
Feature Selection ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Classification Performance ◽  
Feature Reduction ◽  
Security Risk ◽  
Precision Recall Curve ◽  
Iot Devices ◽  
Feature Selection Techniques ◽  
Better Than

AbstractThe recent years have seen a proliferation of Internet of Things (IoT) devices and an associated security risk from an increasing volume of malicious traffic worldwide. For this reason, datasets such as Bot-IoT were created to train machine learning classifiers to identify attack traffic in IoT networks. In this study, we build predictive models with Bot-IoT to detect attacks represented by dataset instances from the Information Theft category, as well as dataset instances from the data exfiltration and keylogging subcategories. Our contribution is centered on the evaluation of ensemble feature selection techniques (FSTs) on classification performance for these specific attack instances. A group or ensemble of FSTs will often perform better than the best individual technique. The classifiers that we use are a diverse set of four ensemble learners (Light GBM, CatBoost, XGBoost, and random forest (RF)) and four non-ensemble learners (logistic regression (LR), decision tree (DT), Naive Bayes (NB), and a multi-layer perceptron (MLP)). The metrics used for evaluating classification performance are area under the receiver operating characteristic curve (AUC) and Area Under the precision-recall curve (AUPRC). For the most part, we determined that our ensemble FSTs do not affect classification performance but are beneficial because feature reduction eases computational burden and provides insight through improved data visualization.

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.

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

2022 ◽  
Vol 23 (1) ◽  
Author(s):  
Li Wang ◽  
Cheng Zhong
Keyword(s):  
Characteristic Curve ◽  
Experimental Results ◽  
Computational Method ◽  
Attention Network ◽  
Feature Vectors ◽  
Disease Similarity ◽  
Potential Association ◽  
Receiver Operation Characteristic ◽  
Disease Pair ◽  
Disease Associations

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.

scholarly journals Predicting miRNA-Disease Associations by Incorporating Projections in Low-Dimensional Space and Local Topological Information

Genes ◽  
2019 ◽  
Vol 10 (9) ◽  
pp. 685 ◽  
Author(s):  
Xuan ◽  
Zhang ◽  
Zhang ◽  
Li ◽  
Zhao
Keyword(s):  
Dimensional Space ◽  
Characteristic Curve ◽  
Feature Space ◽  
Superior Performance ◽  
Topological Information ◽  
Heterogeneous Information ◽  
Feature Representations ◽  
Disease Associations ◽  
Precision Recall Curve ◽  
Low Dimensional

Predicting the potential microRNA (miRNA) candidates associated with a disease helps in exploring the mechanisms of disease development. Most recent approaches have utilized heterogeneous information about miRNAs and diseases, including miRNA similarities, disease similarities, and miRNA-disease associations. However, these methods do not utilize the projections of miRNAs and diseases in a low-dimensional space. Thus, it is necessary to develop a method that can utilize the effective information in the low-dimensional space to predict potential disease-related miRNA candidates. We proposed a method based on non-negative matrix factorization, named DMAPred, to predict potential miRNA-disease associations. DMAPred exploits the similarities and associations of diseases and miRNAs, and it integrates local topological information of the miRNA network. The likelihood that a miRNA is associated with a disease also depends on their projections in low-dimensional space. Therefore, we project miRNAs and diseases into low-dimensional feature space to yield their low-dimensional and dense feature representations. Moreover, the sparse characteristic of miRNA-disease associations was introduced to make our predictive model more credible. DMAPred achieved superior performance for 15 well-characterized diseases with AUCs (area under the receiver operating characteristic curve) ranging from 0.860 to 0.973 and AUPRs (area under the precision-recall curve) ranging from 0.118 to 0.761. In addition, case studies on breast, prostatic, and lung neoplasms demonstrated the ability of DMAPred to discover potential disease-related miRNAs.

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 Seq-SymRF: A random forest model predict potential miRNA-disease associations based on information of sequences and clinical symptoms

2019 ◽  
Author(s):  
Jinlong Li ◽  
Xingyu Chen ◽  
Qixing Huang ◽  
Yang Wang ◽  
Yun Xie ◽  
...  
Keyword(s):  
Random Forest ◽  
Breast Neoplasms ◽  
Clinical Symptoms ◽  
Characteristic Curve ◽  
Vital Role ◽  
New Drugs ◽  
Sequence Information ◽  
Lead Compounds ◽  
Disease Associations

Abstract Background: MicroRNA (MiRNA) plays a vital role in biological processes and closely relate with various human diseases. Research on the miRNA-disease associations contributes to the prevention, diagnosis and treatment of diseases, as well as the identification of new drugs and the discovery of lead compounds. Since traditional experiment methods are time-consuming and expensive, it is necessary to develop an efficient and accurate theoretical approach to identify potential miRNA-disease associations. Results: In this study, a sequence- and symptom-based random forest classifier model (Seq-SymRF) was constructed to identify the potential associations between miRNA and disease. Compared with existing methods, features derived from only sequence information were used to characterize miRNA. More importantly, clinical symptoms were utilized to represent disease. Moreover, the clustering method by calculating the Euclidean distance was implemented to construct the reliable negative sample. Based on the five-fold cross-validation, the model achieved the accuracy, specificity, sensitivity, precision and Matthews correlation coefficient of 98.00%, 99.43%, 96.58%, 99.40% and 0.9604, respectively. The areas under receiver operating characteristic curve and precision recall curve were 0.9967 and 0.9975, respectively. Additionally, case study was implemented with leukemia, breast neoplasms and hsa-mir-21. Most of top-25 predicted disease-related miRNAs (36/50 for leukemia; 33/50 for breast neoplasms) and 32 of top-50 predicted miRNA-related diseases were verified by literature and dbDEMC database. Conclusion: We proposed a new method, named Seq-SymRF, for predicting miRNA-disease associations, which could be regarded as a powerful high-throughput virtual screening tool for drug research and development.

scholarly journals RCMF: a robust collaborative matrix factorization method to predict miRNA-disease associations

2019 ◽  
Vol 20 (S25) ◽  
Author(s):  
Zhen Cui ◽  
Jin-Xing Liu ◽  
Ying-Lian Gao ◽  
Chun-Hou Zheng ◽  
Juan Wang
Keyword(s):  
Matrix Factorization ◽  
Factorization Method ◽  
Simulation Experiments ◽  
Gold Standard Dataset ◽  
Disease Associations ◽  
Auc Value ◽  
Accuracy Of Prediction ◽  
Fold Cross Validation ◽  
Novel Associations ◽  
Better Than

Abstract Background Predicting miRNA-disease associations (MDAs) is time-consuming and expensive. It is imminent to improve the accuracy of prediction results. So it is crucial to develop a novel computing technology to predict new MDAs. Although some existing methods can effectively predict novel MDAs, there are still some shortcomings. Especially when the disease matrix is processed, its sparsity is an important factor affecting the final results. Results A robust collaborative matrix factorization (RCMF) is proposed to predict novel MDAs. The L2,1-norm are introduced to our method to achieve the highest AUC value than other advanced methods. Conclusions 5-fold cross validation is used to evaluate our method, and simulation experiments are used to predict novel associations on Gold Standard Dataset. Finally, our prediction accuracy is better than other existing advanced methods. Therefore, our approach is effective and feasible in predicting novel MDAs.

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