scholarly journals NEMPD: A network embedding-based miRNA-protein-disease network method for the miRNA-disease association prediction

2020 ◽  
Author(s):  
Bo-Ya Ji ◽  
Zhu-Hong You ◽  
Zhan-Heng Chen ◽  
Leon Wong ◽  
Hai-Cheng Yi
Keyword(s):  
Structural Information ◽  
Disease Association ◽  
Human Diseases ◽  
Single Type ◽  
Mirna Sequence ◽  
Sequence Information ◽  
Network Embedding ◽  
Disease Network ◽  
Graph Representations ◽  
Disease Associations

Abstract Background As an important non-coding RNA newly discovered in recent years, MicroRNA (miRNA) plays an important role in a series of life processes and is closely associated with a variety of human diseases. Hence, the identification of potential miRNA-disease associations can make great contributions to the research and treatment of human diseases. However, to our knowledge, many of the existing state-of-the-art computational methods only utilize the single type of known association information between miRNAs and diseases to predict their potential associations, without focusing on their interactions or associations with other types of molecules. Results In this paper, a network embedding-based the tripartite miRNA-protein-disease network (NEMPD) method was proposed for the prediction of miRNA-disease associations. Firstly, a tripartite miRNA-protein-disease network is created by integrating known miRNA-protein and protein-disease associations. Then, we utilize the network representation method-Learning Graph Representations with Global Structural Information (GraRep) to obtain the behavior information (associations with proteins in the network) of miRNAs and diseases. Secondly, the behavior information of miRNAs and diseases is combined with the attribute information of them (disease semantic similarity and miRNA sequence information) to represent miRNA-disease pairs. Thirdly, the prediction model was established based on these known miRNA-disease pairs and the Random Forest algorithm. In the results, under five-fold cross validation, the average prediction accuracy, sensitivity, and AUC of NEMPD is 85.41%, 80.96%, and 91.58%. Furthermore, the performance of NEMPD was also validated by the case studies. Among the top 50 predicted disease-related miRNAs, 48 (breast neoplasms), 47 (colon neoplasms), 47 (lung neoplasms) were confirmed by two other databases. Conclusions NEMPD has a good performance in predicting the potential associations between miRNAs and diseases and has great potency in the field of miRNA-disease association prediction in the future.

scholarly journals NEMPD: A Network Embedding-Based miRNA-Protein-Disease Network Method for the miRNA-Disease Association Prediction

2020 ◽  
Author(s):  
Bo-Ya Ji ◽  
Zhu-Hong You ◽  
Zhan-Heng Chen ◽  
Leon Wong ◽  
Hai-Cheng Yi
Keyword(s):  
Structural Information ◽  
Disease Association ◽  
Human Diseases ◽  
Single Type ◽  
Mirna Sequence ◽  
Sequence Information ◽  
Network Embedding ◽  
Disease Network ◽  
Graph Representations ◽  
Disease Associations

Abstract Background: As an important non-coding RNA newly discovered in recent years, MicroRNA (miRNA) plays an important role in a series of life processes and is closely associated with a variety of human diseases. Hence, the identification of potential miRNA-disease associations can make great contributions to the research and treatment of human diseases. However, to our knowledge, many of the existing state-of-the-art computational methods only utilize the single type of known association information between miRNAs and diseases to predict their potential associations, without focusing on their interactions or associations with other types of molecules.Results: In this paper, a network embedding-based the tripartite miRNA-protein-disease network (NEMPD) method was proposed for the prediction of miRNA-disease associations. Firstly, a tripartite miRNA-protein-disease network is created by integrating known miRNA-protein and protein-disease associations. Then, we utilize the network representation method-Learning Graph Representations with Global Structural Information (GraRep) to obtain the behavior information (associations with proteins in the network) of miRNAs and diseases. Secondly, the behavior information of miRNAs and diseases is combined with the attribute information of them (disease semantic similarity and miRNA sequence information) to represent miRNA-disease pairs. Thirdly, the prediction model was established based on these known miRNA-disease pairs and the Random Forest algorithm. In the results, under five-fold cross validation, the prediction accuracy, sensitivity, and AUC of NEMPD is 85.41%, 80.96%, and 91.58%. Furthermore, the performance of NEMPD was also validated by the case studies. Among the top 50 predicted disease-related miRNAs, 48 (breast neoplasms), 47 (colon neoplasms), 47 (lung neoplasms) were confirmed by two other databases.Conclusions: NEMPD has a good performance in predicting the potential associations between miRNAs and diseases and has great potency in the field of miRNA-disease association prediction in the future.

scholarly journals NEMPD: A network embedding-based method for predicting miRNA-disease associations by preserving behavior and attribute information

2020 ◽  
Author(s):  
Bo-Ya Ji ◽  
Zhu-Hong You ◽  
Zhan-Heng Chen ◽  
Leon Wong ◽  
Hai-Cheng Yi
Keyword(s):  
Structural Information ◽  
Disease Association ◽  
Single Type ◽  
Network Embedding ◽  
Graph Representations ◽  
Non Coding Rna ◽  
Attribute Information ◽  
Disease Associations ◽  
Representation Method ◽  
Great Potency

Abstract Background: As an important non-coding RNA, microRNA (miRNA) plays a significant role in a series of life processes and is closely associated with a variety of Human diseases. Hence, identification of potential miRNA-disease associations can make great contributions to the research and treatment of Human diseases. However, to our knowledge, many existing computational methods only utilize the single type of known association information between miRNAs and diseases to predict their potential associations, without focusing on their interactions or associations with other types of molecules. Results: In this paper, we propose a network embedding-based method for predicting miRNA-disease associations by preserving behavior and attribute information. Firstly, a heterogeneous network is constructed by integrating known associations among miRNA, protein and disease, and the network representation method Learning Graph Representations with Global Structural Information (GraRep) is implemented to learn the behavior information of miRNAs and diseases in the network. Then, the behavior information of miRNAs and diseases is combined with the attribute information of them to represent miRNA-disease association pairs. Finally, the prediction model is established based on the Random Forest algorithm. Under the five-fold cross validation, the proposed NEMPD model obtained average 85.41% prediction accuracy with 80.96% sensitivity at the AUC of 91.58%. Furthermore, the performance of NEMPD is also validated by the case studies. Among the top 50 predicted disease-related miRNAs, 48 (breast neoplasms), 47 (colon neoplasms), 47 (lung neoplasms) were confirmed by two other databases.Conclusions: The proposed NEMPD model has a good performance in predicting the potential associations between miRNAs and diseases, and has great potency in the field of miRNA-disease association prediction in the future.

scholarly journals NEMPD: a network embedding-based method for predicting miRNA-disease associations by preserving behavior and attribute information

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Bo-Ya Ji ◽  
Zhu-Hong You ◽  
Zhan-Heng Chen ◽  
Leon Wong ◽  
Hai-Cheng Yi
Keyword(s):  
Structural Information ◽  
Disease Association ◽  
Single Type ◽  
Network Embedding ◽  
Graph Representations ◽  
Non Coding Rna ◽  
Attribute Information ◽  
Disease Associations ◽  
Representation Method ◽  
Great Potency

Abstract Background As an important non-coding RNA, microRNA (miRNA) plays a significant role in a series of life processes and is closely associated with a variety of Human diseases. Hence, identification of potential miRNA-disease associations can make great contributions to the research and treatment of Human diseases. However, to our knowledge, many existing computational methods only utilize the single type of known association information between miRNAs and diseases to predict their potential associations, without focusing on their interactions or associations with other types of molecules. Results In this paper, we propose a network embedding-based method for predicting miRNA-disease associations by preserving behavior and attribute information. Firstly, a heterogeneous network is constructed by integrating known associations among miRNA, protein and disease, and the network representation method Learning Graph Representations with Global Structural Information (GraRep) is implemented to learn the behavior information of miRNAs and diseases in the network. Then, the behavior information of miRNAs and diseases is combined with the attribute information of them to represent miRNA-disease association pairs. Finally, the prediction model is established based on the Random Forest algorithm. Under the five-fold cross validation, the proposed NEMPD model obtained average 85.41% prediction accuracy with 80.96% sensitivity at the AUC of 91.58%. Furthermore, the performance of NEMPD is also validated by the case studies. Among the top 50 predicted disease-related miRNAs, 48 (breast neoplasms), 47 (colon neoplasms), 47 (lung neoplasms) were confirmed by two other databases. Conclusions The proposed NEMPD model has a good performance in predicting the potential associations between miRNAs and diseases, and has great potency in the field of miRNA-disease association prediction in the future.

scholarly journals Predicting miRNA-Disease Association Based on Modularity Preserving Heterogeneous Network Embedding

2021 ◽  
Vol 9 ◽  
Author(s):  
Wei Peng ◽  
Jielin Du ◽  
Wei Dai ◽  
Wei Lan
Keyword(s):  
Heterogeneous Network ◽  
Structural Information ◽  
Disease Diagnosis ◽  
Disease Association ◽  
Biological Information ◽  
Vector Representation ◽  
Network Embedding ◽  
Prediction Ability ◽  
Similarity Network ◽  
Disease Associations

MicroRNAs (miRNAs) are a category of small non-coding RNAs that profoundly impact various biological processes related to human disease. Inferring the potential miRNA-disease associations benefits the study of human diseases, such as disease prevention, disease diagnosis, and drug development. In this work, we propose a novel heterogeneous network embedding-based method called MDN-NMTF (Module-based Dynamic Neighborhood Non-negative Matrix Tri-Factorization) for predicting miRNA-disease associations. MDN-NMTF constructs a heterogeneous network of disease similarity network, miRNA similarity network and a known miRNA-disease association network. After that, it learns the latent vector representation for miRNAs and diseases in the heterogeneous network. Finally, the association probability is computed by the product of the latent miRNA and disease vectors. MDN-NMTF not only successfully integrates diverse biological information of miRNAs and diseases to predict miRNA-disease associations, but also considers the module properties of miRNAs and diseases in the course of learning vector representation, which can maximally preserve the heterogeneous network structural information and the network properties. At the same time, we also extend MDN-NMTF to a new version (called MDN-NMTF2) by using modular information to improve the miRNA-disease association prediction ability. Our methods and the other four existing methods are applied to predict miRNA-disease associations in four databases. The prediction results show that our methods can improve the miRNA-disease association prediction to a high level compared with the four existing methods.

iPiDi-PUL: identifying Piwi-interacting RNA-disease associations based on positive unlabeled learning

2020 ◽  
Author(s):  
Hang Wei ◽  
Yong Xu ◽  
Bin Liu
Keyword(s):  
Low Cost ◽  
Principal Component ◽  
Disease Association ◽  
Biological Data ◽  
Sequence Information ◽  
Learning Approach ◽  
Challenging Problem ◽  
Disease Treatment ◽  
Disease Associations ◽  
Key Features

Abstract Accumulated researches have revealed that Piwi-interacting RNAs (piRNAs) are regulating the development of germ and stem cells, and they are closely associated with the progression of many diseases. As the number of the detected piRNAs is increasing rapidly, it is important to computationally identify new piRNA-disease associations with low cost and provide candidate piRNA targets for disease treatment. However, it is a challenging problem to learn effective association patterns from the positive piRNA-disease associations and the large amount of unknown piRNA-disease pairs. In this study, we proposed a computational predictor called iPiDi-PUL to identify the piRNA-disease associations. iPiDi-PUL extracted the features of piRNA-disease associations from three biological data sources, including piRNA sequence information, disease semantic terms and the available piRNA-disease association network. Principal component analysis (PCA) was then performed on these features to extract the key features. The training datasets were constructed based on known positive associations and the negative associations selected from the unknown pairs. Various random forest classifiers trained with these different training sets were merged to give the predictive results via an ensemble learning approach. Finally, the web server of iPiDi-PUL was established at http://bliulab.net/iPiDi-PUL to help the researchers to explore the associated diseases for newly discovered piRNAs.

scholarly journals A Novel Network-Based Computational Model for Prediction of Potential LncRNA–Disease Association

2019 ◽  
Vol 20 (7) ◽  
pp. 1549 ◽  
Author(s):  
Yang Liu ◽  
Xiang Feng ◽  
Haochen Zhao ◽  
Zhanwei Xuan ◽  
Lei Wang
Keyword(s):  
Computational Models ◽  
Disease Association ◽  
Label Propagation ◽  
Human Diseases ◽  
Weighted Matrix ◽  
Disease Associations ◽  
Resource Allocation Strategy ◽  
Simulation Results ◽  
Leave One Out ◽  
Treatment Prognosis

Accumulating studies have shown that long non-coding RNAs (lncRNAs) are involved in many biological processes and play important roles in a variety of complex human diseases. Developing effective computational models to identify potential relationships between lncRNAs and diseases can not only help us understand disease mechanisms at the lncRNA molecular level, but also promote the diagnosis, treatment, prognosis, and prevention of human diseases. For this paper, a network-based model called NBLDA was proposed to discover potential lncRNA–disease associations, in which two novel lncRNA–disease weighted networks were constructed. They were first based on known lncRNA–disease associations and topological similarity of the lncRNA–disease association network, and then an lncRNA–lncRNA weighted matrix and a disease–disease weighted matrix were obtained based on a resource allocation strategy of unequal allocation and unbiased consistence. Finally, a label propagation algorithm was applied to predict associated lncRNAs for the investigated diseases. Moreover, in order to estimate the prediction performance of NBLDA, the framework of leave-one-out cross validation (LOOCV) was implemented on NBLDA, and simulation results showed that NBLDA can achieve reliable areas under the ROC curve (AUCs) of 0.8846, 0.8273, and 0.8075 in three known lncRNA–disease association datasets downloaded from the lncRNADisease database, respectively. Furthermore, in case studies of lung cancer, leukemia, and colorectal cancer, simulation results demonstrated that NBLDA can be a powerful tool for identifying potential lncRNA–disease associations as well.

scholarly journals ILPMDA: Predicting miRNA–Disease Association Based on Improved Label Propagation

2021 ◽  
Vol 12 ◽  
Author(s):  
Yu-Tian Wang ◽  
Lei Li ◽  
Cun-Mei Ji ◽  
Chun-Hou Zheng ◽  
Jian-Cheng Ni
Keyword(s):  
Disease Association ◽  
Label Propagation ◽  
Human Diseases ◽  
Biological Information ◽  
Disease Similarity ◽  
Kernel Fusion ◽  
Disease Associations ◽  
Non Coding Rnas ◽  
Similarity Networks ◽  
Association Data

MicroRNAs (miRNAs) are small non-coding RNAs that have been demonstrated to be related to numerous complex human diseases. Considerable studies have suggested that miRNAs affect many complicated bioprocesses. Hence, the investigation of disease-related miRNAs by utilizing computational methods is warranted. In this study, we presented an improved label propagation for miRNA–disease association prediction (ILPMDA) method to observe disease-related miRNAs. First, we utilized similarity kernel fusion to integrate different types of biological information for generating miRNA and disease similarity networks. Second, we applied the weighted k-nearest known neighbor algorithm to update verified miRNA–disease association data. Third, we utilized improved label propagation in disease and miRNA similarity networks to make association prediction. Furthermore, we obtained final prediction scores by adopting an average ensemble method to integrate the two kinds of prediction results. To evaluate the prediction performance of ILPMDA, two types of cross-validation methods and case studies on three significant human diseases were implemented to determine the accuracy and effectiveness of ILPMDA. All results demonstrated that ILPMDA had the ability to discover potential miRNA–disease associations.

scholarly journals DEMLP: DeepWalk Embedding in MLP for miRNA-Disease Association Prediction

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Xun Wang ◽  
Fuyu Wang ◽  
Xinzeng Wang ◽  
Sibo Qiao ◽  
Yu Zhuang
Keyword(s):  
Multilayer Perceptron ◽  
High Efficiency ◽  
Interaction Network ◽  
Disease Association ◽  
Biological Processes ◽  
Disease Network ◽  
Graph Node ◽  
Disease Associations ◽  
Auc Value ◽  
Low Dimensional

miRNAs significantly affect multifarious biological processes involving human disease. Biological experiments always need enormous financial support and time cost. Taking expense and difficulty into consideration, to predict the potential miRNA-disease associations, a lot of high-efficiency computational methods by computer have been developed, based on a network generated by miRNA-disease association dataset. However, there exist many challenges. Firstly, the association between miRNAs and diseases is intricate. These methods should consider the influence of the neighborhoods of each node from the network. Secondly, how to measure whether there is an association between two nodes of the network is also an important problem. In our study, we innovatively integrate graph node embedding with a multilayer perceptron and propose a method DEMLP. To begin with, we construct a miRNA-disease network by miRNA-disease adjacency matrix (MDA). Then, low-dimensional embedding representation vectors of nodes are learned from the miRNA-disease network by DeepWalk. Finally, we use these low-dimensional embedding representation vectors as input to train the multilayer perceptron. Experiments show that our proposed method that only utilized the miRNA–disease association information can effectively predict miRNA-disease associations. To evaluate the effectiveness of DEMLP in a miRNA-disease network from HMDD v3.2, we apply fivefold crossvalidation in our study. The ROC-AUC computed result value of DEMLP is 0.943, and the PR-AUC value of DEMLP is 0.937. Compared with other state-of-the-art methods, our method shows good performance using only the miRNA-disease interaction network.

scholarly journals A network embedding-based multiple information integration method for the MiRNA-disease association prediction

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Yuchong Gong ◽  
Yanqing Niu ◽  
Wen Zhang ◽  
Xiaohong Li
Keyword(s):  
Information Integration ◽  
Integration Method ◽  
State Of The Art ◽  
Mirna Family ◽  
Disease Association ◽  
Bipartite Network ◽  
Network Embedding ◽  
Embedding Method ◽  
Disease Associations ◽  
Family Associations

Abstract Background MiRNAs play significant roles in many fundamental and important biological processes, and predicting potential miRNA-disease associations makes contributions to understanding the molecular mechanism of human diseases. Existing state-of-the-art methods make use of miRNA-target associations, miRNA-family associations, miRNA functional similarity, disease semantic similarity and known miRNA-disease associations, but the known miRNA-disease associations are not well exploited. Results In this paper, a network embedding-based multiple information integration method (NEMII) is proposed for the miRNA-disease association prediction. First, known miRNA-disease associations are formulated as a bipartite network, and the network embedding method Structural Deep Network Embedding (SDNE) is adopted to learn embeddings of nodes in the bipartite network. Second, the embedding representations of miRNAs and diseases are combined with biological features about miRNAs and diseases (miRNA-family associations and disease semantic similarities) to represent miRNA-disease pairs. Third, the prediction models are constructed based on the miRNA-disease pairs by using the random forest. In computational experiments, NEMII achieves high-accuracy performances and outperforms other state-of-the-art methods: GRNMF, NTSMDA and PBMDA. The usefulness of NEMII is further validated by case studies. The studies demonstrate the great potential of network embedding method for the miRNA-disease association prediction, and SDNE outperforms other popular network embedding methods: DeepWalk, High-Order Proximity preserved Embedding (HOPE) and Laplacian Eigenmaps (LE). Conclusion We propose a new method, named NEMII, for predicting miRNA-disease associations, which has great potential to benefit the field of miRNA-disease association prediction.

NCMCMDA: miRNA–disease association prediction through neighborhood constraint matrix completion

2020 ◽  
Author(s):  
Xing Chen ◽  
Lian-Gang Sun ◽  
Yan Zhao
Keyword(s):  
Computational Models ◽  
Cross Validation ◽  
Matrix Completion ◽  
Critical Role ◽  
Disease Association ◽  
Human Diseases ◽  
Superior Performance ◽  
Main Task ◽  
Constraint Matrix ◽  
Disease Associations

Abstract Emerging evidence shows that microRNAs (miRNAs) play a critical role in diverse fundamental and important biological processes associated with human diseases. Inferring potential disease related miRNAs and employing them as the biomarkers or drug targets could contribute to the prevention, diagnosis and treatment of complex human diseases. In view of that traditional biological experiments cost much time and resources, computational models would serve as complementary means to uncover potential miRNA–disease associations. In this study, we proposed a new computational model named Neighborhood Constraint Matrix Completion for MiRNA–Disease Association prediction (NCMCMDA) to predict potential miRNA–disease associations. The main task of NCMCMDA was to recover the missing miRNA–disease associations based on the known miRNA–disease associations and integrated disease (miRNA) similarity. In this model, we innovatively integrated neighborhood constraint with matrix completion, which provided a novel idea of utilizing similarity information to assist the prediction. After the recovery task was transformed into an optimization problem, we solved it with a fast iterative shrinkage-thresholding algorithm. As a result, the AUCs of NCMCMDA in global and local leave-one-out cross validation were 0.9086 and 0.8453, respectively. In 5-fold cross validation, NCMCMDA achieved an average AUC of 0.8942 and standard deviation of 0.0015, which demonstrated NCMCMDA’s superior performance than many previous computational methods. Furthermore, NCMCMDA was applied to three different types of case studies to further evaluate its prediction reliability and accuracy. As a result, 84% (colon neoplasms), 98% (esophageal neoplasms) and 98% (breast neoplasms) of the top 50 predicted miRNAs were verified by recent literature.

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