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.

scholarly journals A Novel Computational Model for Predicting Potential LncRNA-Disease Associations based on Both Direct and Indirect Features of LncRNA-Disease Pairs

2019 ◽  
Author(s):  
Yubin Xiao ◽  
Zheng Xiao ◽  
Xiang Feng ◽  
Zhiping Chen ◽  
Linai Kuang ◽  
...  
Keyword(s):  
Computational Model ◽  
Cross Validation ◽  
Single Model ◽  
Average Case ◽  
Disease Associations ◽  
Artificial Neural Network Ann ◽  
Prediction Techniques ◽  
Leave One Out ◽  
Fold Cross Validation

Abstract BackgroundAccumulating evidence has demonstrated that lncRNAs are closely associated with human diseases, and it is helpful for the diagnosis and treatment of diseases to get the relationships between lncRNAs and diseases. Due to the high costs and time complexity of traditional bio-experiments, in recent years, more and more computational methods have been proposed by researchers to infer potential lncRNA-disease associations. However, there exist all kinds of limitations in these prediction methods as well. ResultsIn this manuscript, a novel computational model named FVTLDA is proposed to infer potential lncRNA-disease associations. In FVTLDA, its major novelty lies in the integration of direct and indirect features related to lncRNA-disease associations such as the feature vectors of lncRNA-disease pairs and their corresponding association probability fractions, which guarantees that FVTLDA can be utilized to predict diseases without known related-lncRNAs and lncRNAs without known related-diseases. Moreover, FVTLDA neither relies solely on known lncRNA-disease nor requires any negative samples, which guarantee that it can infer potential lncRNA-disease associations more equitably and effectively than traditional methods. Additionally, to avoid the limitations of single model prediction techniques, we combine FVTLDA with the Multiple Linear Regression (MLR) and the Artificial Neural Network (ANN) for data analysis respectively. Simulation experiment results show that FVTLDA with MLR can achieve reliable AUCs of 0.8909, 0.8936 and 0.8970 in 5-Fold Cross Validation, 10-Fold Cross Validation and Leave-One-Out Cross Validation, separately, while FVTLDA with ANN can achieve reliable AUCs of 0.8766, 0.8830 and 0.8807 in 5-fold CV, 10-fold CV, and LOOCV respectively. Furthermore, in case studies of gastric cancer, leukemia and lung cancer, experiment results show that there are 8, 8 and 8 out of top 10 candidate lncRNAs predicted by FVTLDA with MLR, and 8, 7 and 8 out of top 10 candidate lncRNAs predicted by FVTLDA with ANN, having been verified by recent literature. Moreover, comparing with the representative prediction model of KATZLDA, results illustrate that FVTLDA with MLR and FVTLDA with ANN can achieve the average case study contrast scores of 0.8429 and 0.8515 respectively, which are both higher than the average case study contrast score of 0.6375 achieved by KATZLDA.

scholarly journals WBNPMD: weighted bipartite network projection for microRNA-disease association prediction

2019 ◽  
Vol 17 (1) ◽  
Author(s):  
Guobo Xie ◽  
Zhiliang Fan ◽  
Yuping Sun ◽  
Cuiming Wu ◽  
Lei Ma
Keyword(s):  
Cross Validation ◽  
Lung Neoplasm ◽  
Disease Association ◽  
Computational Techniques ◽  
Bipartite Network ◽  
Initial Information ◽  
Method Transfer ◽  
Disease Associations ◽  
Association Data ◽  
Leave One Out

Abstract Background Recently, numerous biological experiments have indicated that microRNAs (miRNAs) play critical roles in exploring the pathogenesis of various human diseases. Since traditional experimental methods for miRNA-disease associations detection are costly and time-consuming, it becomes urgent to design efficient and robust computational techniques for identifying undiscovered interactions. Methods In this paper, we proposed a computation framework named weighted bipartite network projection for miRNA-disease association prediction (WBNPMD). In this method, transfer weights were constructed by combining the known miRNA and disease similarities, and the initial information was properly configured. Then the two-step bipartite network algorithm was implemented to infer potential miRNA-disease associations. Results The proposed WBNPMD was applied to the known miRNA-disease association data, and leave-one-out cross-validation (LOOCV) and fivefold cross-validation were implemented to evaluate the performance of WBNPMD. As a result, our method achieved the AUCs of 0.9321 and $$0.9173 \pm 0.0005$$ 0.9173 ± 0.0005 in LOOCV and fivefold cross-validation, and outperformed other four state-of-the-art methods. We also carried out two kinds of case studies on prostate neoplasm, colorectal neoplasm, and lung neoplasm, and most of the top 50 predicted miRNAs were confirmed to have an association with the corresponding diseases based on dbDeMC, miR2Disease, and HMDD V3.0 databases. Conclusions The experimental results demonstrate that WBNPMD can accurately infer potential miRNA-disease associations. We anticipated that the proposed WBNPMD could serve as a powerful tool for potential miRNA-disease associations excavation.

scholarly journals A novel computational model for predicting potential LncRNA-disease associations based on both direct and indirect features of LncRNA-disease pairs

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Yubin Xiao ◽  
Zheng Xiao ◽  
Xiang Feng ◽  
Zhiping Chen ◽  
Linai Kuang ◽  
...  
Keyword(s):  
Computational Model ◽  
Cross Validation ◽  
State Of The Art ◽  
Prediction Methods ◽  
Good Prediction ◽  
Average Case ◽  
Comparison Results ◽  
Disease Associations ◽  
Fold Cross Validation

Abstract Background Accumulating evidence has demonstrated that long non-coding RNAs (lncRNAs) are closely associated with human diseases, and it is useful for the diagnosis and treatment of diseases to get the relationships between lncRNAs and diseases. Due to the high costs and time complexity of traditional bio-experiments, in recent years, more and more computational methods have been proposed by researchers to infer potential lncRNA-disease associations. However, there exist all kinds of limitations in these state-of-the-art prediction methods as well. Results In this manuscript, a novel computational model named FVTLDA is proposed to infer potential lncRNA-disease associations. In FVTLDA, its major novelty lies in the integration of direct and indirect features related to lncRNA-disease associations such as the feature vectors of lncRNA-disease pairs and their corresponding association probability fractions, which guarantees that FVTLDA can be utilized to predict diseases without known related-lncRNAs and lncRNAs without known related-diseases. Moreover, FVTLDA neither relies solely on known lncRNA-disease nor requires any negative samples, which guarantee that it can infer potential lncRNA-disease associations more equitably and effectively than traditional state-of-the-art prediction methods. Additionally, to avoid the limitations of single model prediction techniques, we combine FVTLDA with the Multiple Linear Regression (MLR) and the Artificial Neural Network (ANN) for data analysis respectively. Simulation experiment results show that FVTLDA with MLR can achieve reliable AUCs of 0.8909, 0.8936 and 0.8970 in 5-Fold Cross Validation (fivefold CV), 10-Fold Cross Validation (tenfold CV) and Leave-One-Out Cross Validation (LOOCV), separately, while FVTLDA with ANN can achieve reliable AUCs of 0.8766, 0.8830 and 0.8807 in fivefold CV, tenfold CV, and LOOCV respectively. Furthermore, in case studies of gastric cancer, leukemia and lung cancer, experiment results show that there are 8, 8 and 8 out of top 10 candidate lncRNAs predicted by FVTLDA with MLR, and 8, 7 and 8 out of top 10 candidate lncRNAs predicted by FVTLDA with ANN, having been verified by recent literature. Comparing with the representative prediction model of KATZLDA, comparison results illustrate that FVTLDA with MLR and FVTLDA with ANN can achieve the average case study contrast scores of 0.8429 and 0.8515 respectively, which are both notably higher than the average case study contrast score of 0.6375 achieved by KATZLDA. Conclusion The simulation results show that FVTLDA has good prediction performance, which is a good supplement to future bioinformatics research.

scholarly journals A Novel Computational Model for Predicting Potential LncRNA-Disease Associations based on Both Direct and Indirect Features of LncRNA-Disease Pairs

2020 ◽  
Author(s):  
Yubin Xiao ◽  
Zheng Xiao ◽  
Xiang Feng ◽  
Zhiping Chen ◽  
Linai Kuang ◽  
...  
Keyword(s):  
Computational Model ◽  
Cross Validation ◽  
State Of The Art ◽  
Prediction Methods ◽  
Good Prediction ◽  
Average Case ◽  
Comparison Results ◽  
Disease Associations ◽  
Fold Cross Validation

Abstract Background: Accumulating evidence has demonstrated that long non-coding RNAs (lncRNAs) are closely associated with human diseases, and it is useful for the diagnosis and treatment of diseases to get the relationships between lncRNAs and diseases. Due to the high costs and time complexity of traditional bio-experiments, in recent years, more and more computational methods have been proposed by researchers to infer potential lncRNA-disease associations. However, there exist all kinds of limitations in these state-of-the-art prediction methods as well.Results: In this manuscript, a novel computational model named FVTLDA is proposed to infer potential lncRNA-disease associations. In FVTLDA, its major novelty lies in the integration of direct and indirect features related to lncRNA-disease associations such as the feature vectors of lncRNA-disease pairs and their corresponding association probability fractions, which guarantees that FVTLDA can be utilized to predict diseases without known related-lncRNAs and lncRNAs without known related-diseases. Moreover, FVTLDA neither relies solely on known lncRNA-disease nor requires any negative samples, which guarantee that it can infer potential lncRNA-disease associations more equitably and effectively than traditional state-of-the-art prediction methods. Additionally, to avoid the limitations of single model prediction techniques, we combine FVTLDA with the Multiple Linear Regression (MLR) and the Artificial Neural Network (ANN) for data analysis respectively. Simulation experiment results show that FVTLDA with MLR can achieve reliable AUCs of 0.8909, 0.8936 and 0.8970 in 5-Fold Cross Validation (5-fold CV), 10-Fold Cross Validation (10-fold CV) and Leave-One-Out Cross Validation (LOOCV), separately, while FVTLDA with ANN can achieve reliable AUCs of 0.8766, 0.8830 and 0.8807 in 5-fold CV, 10-fold CV, and LOOCV respectively. Furthermore, in case studies of gastric cancer, leukemia and lung cancer, experiment results show that there are 8, 8 and 8 out of top 10 candidate lncRNAs predicted by FVTLDA with MLR, and 8, 7 and 8 out of top 10 candidate lncRNAs predicted by FVTLDA with ANN, having been verified by recent literature. Comparing with the representative prediction model of KATZLDA, comparison results illustrate that FVTLDA with MLR and FVTLDA with ANN can achieve the average case study contrast scores of 0.8429 and 0.8515 respectively, which are both notably higher than the average case study contrast score of 0.6375 achieved by KATZLDA.Conclusion: The simulation results show that FVTLDA has good prediction performance, which is a good supplement to future bioinformatics research.

scholarly journals Prediction of Potential miRNA–Disease Associations Through a Novel Unsupervised Deep Learning Framework with Variational Autoencoder

Cells ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 1040 ◽  
Author(s):  
Li Zhang ◽  
Xing Chen ◽  
Jun Yin
Keyword(s):  
Deep Learning ◽  
Cross Validation ◽  
Operating Characteristics ◽  
Learning Framework ◽  
Disease Similarity ◽  
Variational Autoencoder ◽  
Disease Associations ◽  
Unsupervised Deep Learning ◽  
Leave One Out

The important role of microRNAs (miRNAs) in the formation, development, diagnosis, and treatment of diseases has attracted much attention among researchers recently. In this study, we present an unsupervised deep learning model of the variational autoencoder for MiRNA–disease association prediction (VAEMDA). Through combining the integrated miRNA similarity and the integrated disease similarity with known miRNA–disease associations, respectively, we constructed two spliced matrices. These matrices were applied to train the variational autoencoder (VAE), respectively. The final predicted association scores between miRNAs and diseases were obtained by integrating the scores from the two trained VAE models. Unlike previous models, VAEMDA can avoid noise introduced by the random selection of negative samples and reveal associations between miRNAs and diseases from the perspective of data distribution. Compared with previous methods, VAEMDA obtained higher area under the receiver operating characteristics curves (AUCs) of 0.9118, 0.8652, and 0.9091 ± 0.0065 in global leave-one-out cross validation (LOOCV), local LOOCV, and five-fold cross validation, respectively. Further, the AUCs of VAEMDA were 0.8250 and 0.8237 in global leave-one-disease-out cross validation (LODOCV), and local LODOCV, respectively. In three different types of case studies on three important diseases, the results showed that most of the top 50 potentially associated miRNAs were verified by databases and the literature.

Deep-belief network for predicting potential miRNA-disease associations

2020 ◽  
Author(s):  
Xing Chen ◽  
Tian-Hao Li ◽  
Yan Zhao ◽  
Chun-Chun Wang ◽  
Chi-Chi Zhu
Keyword(s):  
Computational Models ◽  
Cross Validation ◽  
Biological Data ◽  
Deep Belief Network ◽  
Computational Method ◽  
Restricted Boltzmann Machines ◽  
Belief Network ◽  
Disease Associations ◽  
Leave One Out ◽  
The Impact

Abstract MicroRNA (miRNA) plays an important role in the occurrence, development, diagnosis and treatment of diseases. More and more researchers begin to pay attention to the relationship between miRNA and disease. Compared with traditional biological experiments, computational method of integrating heterogeneous biological data to predict potential associations can effectively save time and cost. Considering the limitations of the previous computational models, we developed the model of deep-belief network for miRNA-disease association prediction (DBNMDA). We constructed feature vectors to pre-train restricted Boltzmann machines for all miRNA-disease pairs and applied positive samples and the same number of selected negative samples to fine-tune DBN to obtain the final predicted scores. Compared with the previous supervised models that only use pairs with known label for training, DBNMDA innovatively utilizes the information of all miRNA-disease pairs during the pre-training process. This step could reduce the impact of too few known associations on prediction accuracy to some extent. DBNMDA achieves the AUC of 0.9104 based on global leave-one-out cross validation (LOOCV), the AUC of 0.8232 based on local LOOCV and the average AUC of 0.9048 ± 0.0026 based on 5-fold cross validation. These AUCs are better than other previous models. In addition, three different types of case studies for three diseases were implemented to demonstrate the accuracy of DBNMDA. As a result, 84% (breast neoplasms), 100% (lung neoplasms) and 88% (esophageal neoplasms) of the top 50 predicted miRNAs were verified by recent literature. Therefore, we could conclude that DBNMDA is an effective method to predict potential miRNA-disease associations.

scholarly journals Prediction of MicroRNA-Disease Associations Based on Social Network Analysis Methods

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Quan Zou ◽  
Jinjin Li ◽  
Qingqi Hong ◽  
Ziyu Lin ◽  
Yun Wu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Cross Validation ◽  
Supervised Machine Learning ◽  
Rna Molecules ◽  
Disease Associations ◽  
Endogenous Genes ◽  
Leave One Out

MicroRNAs constitute an important class of noncoding, single-stranded, ~22 nucleotide long RNA molecules encoded by endogenous genes. They play an important role in regulating gene transcription and the regulation of normal development. MicroRNAs can be associated with disease; however, only a few microRNA-disease associations have been confirmed by traditional experimental approaches. We introduce two methods to predict microRNA-disease association. The first method, KATZ, focuses on integrating the social network analysis method with machine learning and is based on networks derived from known microRNA-disease associations, disease-disease associations, and microRNA-microRNA associations. The other method, CATAPULT, is a supervised machine learning method. We applied the two methods to 242 known microRNA-disease associations and evaluated their performance using leave-one-out cross-validation and 3-fold cross-validation. Experiments proved that our methods outperformed the state-of-the-art methods.

scholarly journals MDAKRLS: Predicting human microbe-disease association based on Kronecker regularized least squares and similarities

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Da Xu ◽  
Hanxiao Xu ◽  
Yusen Zhang ◽  
Mingyi Wang ◽  
Wei Chen ◽  
...  
Keyword(s):  
Least Squares ◽  
Cross Validation ◽  
Computational Method ◽  
Human Diseases ◽  
Regularized Least Squares ◽  
Comparison Results ◽  
Pathological Mechanism ◽  
Disease Associations ◽  
Inflammatory Bowel ◽  
Leave One Out

Abstract Background Microbes are closely related to human health and diseases. Identification of disease-related microbes is of great significance for revealing the pathological mechanism of human diseases and understanding the interaction mechanisms between microbes and humans, which is also useful for the prevention, diagnosis and treatment of human diseases. Considering the known disease-related microbes are still insufficient, it is necessary to develop effective computational methods and reduce the time and cost of biological experiments. Methods In this work, we developed a novel computational method called MDAKRLS to discover potential microbe-disease associations (MDAs) based on the Kronecker regularized least squares. Specifically, we introduced the Hamming interaction profile similarity to measure the similarities of microbes and diseases besides Gaussian interaction profile kernel similarity. In addition, we introduced the Kronecker product to construct two kinds of Kronecker similarities between microbe-disease pairs. Then, we designed the Kronecker regularized least squares with different Kronecker similarities to obtain prediction scores, respectively, and calculated the final prediction scores by integrating the contributions of different similarities. Results The AUCs value of global leave-one-out cross-validation and 5-fold cross-validation achieved by MDAKRLS were 0.9327 and 0.9023 ± 0.0015, which were significantly higher than five state-of-the-art methods used for comparison. Comparison results demonstrate that MDAKRLS has faster computing speed under two kinds of frameworks. In addition, case studies of inflammatory bowel disease (IBD) and asthma further showed 19 (IBD), 19 (asthma) of the top 20 prediction disease-related microbes could be verified by previously published biological or medical literature. Conclusions All the evaluation results adequately demonstrated that MDAKRLS has an effective and reliable prediction performance. It may be a useful tool to seek disease-related new microbes and help biomedical researchers to carry out follow-up studies.

scholarly journals Algebraic Shortcuts for Leave-One-Out Cross-Validation in Supervised Network Inference

2018 ◽  
Author(s):  
Michiel Stock ◽  
Tapio Pahikkala ◽  
Antti Airola ◽  
Willem Waegeman ◽  
Bernard De Baets
Keyword(s):  
Machine Learning ◽  
Biological Networks ◽  
Regulatory Networks ◽  
Network Inference ◽  
Cross Validation ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Ligand Interaction ◽  
Learning Techniques ◽  
Leave One Out

AbstractMotivationSupervised machine learning techniques have traditionally been very successful at reconstructing biological networks, such as protein-ligand interaction, protein-protein interaction and gene regulatory networks. Recently, much emphasis has been placed on the correct evaluation of such supervised models. It is vital to distinguish between using the model to either predict new interactions in a given network or to predict interactions for a new vertex not present in the original network. Specific cross-validation schemes need to be used to assess the performance in such different prediction settings.ResultsWe present a series of leave-one-out cross-validation shortcuts to rapidly estimate the performance of state-of-the-art kernel-based network inference techniques.AvailabilityThe machine learning techniques with the algebraic shortcuts are implemented in the RLScore software package.

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.

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