scholarly journals A network similarity integration method for predicting microRNA-disease associations

RSC Advances ◽  
2017 ◽  
Vol 7 (51) ◽  
pp. 32216-32224 ◽  
Author(s):  
Xiaoying Li ◽  
Yaping Lin ◽  
Changlong Gu
Keyword(s):  
Integration Method ◽  
Disease Association ◽  
Association Network ◽  
Similarity Network ◽  
Novel Mirna ◽  
Disease Similarity ◽  
Disease Associations ◽  
Network Similarity

The NSIM integrates the disease similarity network, miRNA similarity network, and known miRNA-disease association network on the basis of cousin similarity to predict not only novel miRNA-disease associations but also isolated diseases.

scholarly journals Predicting Drug-Disease Association Based on Ensemble Strategy

2021 ◽  
Vol 12 ◽  
Author(s):  
Jianlin Wang ◽  
Wenxiu Wang ◽  
Chaokun Yan ◽  
Junwei Luo ◽  
Ge Zhang
Keyword(s):  
Prediction Models ◽  
Drug Repositioning ◽  
Predictive Ability ◽  
Disease Association ◽  
New Drugs ◽  
Similarity Network ◽  
Disease Similarity ◽  
Ensemble Strategy ◽  
Disease Associations ◽  
Reducing Costs

Drug repositioning is used to find new uses for existing drugs, effectively shortening the drug research and development cycle and reducing costs and risks. A new model of drug repositioning based on ensemble learning is proposed. This work develops a novel computational drug repositioning approach called CMAF to discover potential drug-disease associations. First, for new drugs and diseases or unknown drug-disease pairs, based on their known neighbor information, an association probability can be obtained by implementing the weighted K nearest known neighbors (WKNKN) method and improving the drug-disease association information. Then, a new drug similarity network and new disease similarity network can be constructed. Three prediction models are applied and ensembled to enable the final association of drug-disease pairs based on improved drug-disease association information and the constructed similarity network. The experimental results demonstrate that the developed approach outperforms recent state-of-the-art prediction models. Case studies further confirm the predictive ability of the proposed method. Our proposed method can effectively improve the prediction results.

Identifying human microRNA–disease associations by a new diffusion-based method

2015 ◽  
Vol 13 (04) ◽  
pp. 1550014 ◽  
Author(s):  
Bo Liao ◽  
Sumei Ding ◽  
Haowen Chen ◽  
Zejun Li ◽  
Lijun Cai
Keyword(s):  
Biomedical Research ◽  
Prediction Accuracy ◽  
Information Sources ◽  
Cross Validation ◽  
Disease Association ◽  
Global Network ◽  
Disease Similarity ◽  
Disease Associations ◽  
Network Similarity ◽  
Leave One Out

Identifying the microRNA–disease relationship is vital for investigating the pathogenesis of various diseases. However, experimental verification of disease-related microRNAs remains considerable challenge to many researchers, particularly for the fact that numerous new microRNAs are discovered every year. As such, development of computational methods for disease-related microRNA prediction has recently gained eminent attention. In this paper, first, we construct a miRNA functional network and a disease similarity network by integrating different information sources. Then, we further introduce a new diffusion-based method (NDBM) to explore global network similarity for miRNA–disease association inference. Even though known miRNA–disease associations in the database are rare, NDBM still achieves an area under the ROC curve (AUC) of 85.62% in the leave-one-out cross-validation in improving the prediction accuracy of previous methods significantly. Moreover, our method is applicable to diseases with no known related miRNAs as well as new miRNAs with unknown target diseases. Some associations who strongly predicted by our method are confirmed by public databases. These superior performances suggest that NDBM could be an effective and important tool for biomedical research.

scholarly journals Network-based collaborative filtering recommendation model for inferring novel disease-related miRNAs

RSC Advances ◽  
2017 ◽  
Vol 7 (71) ◽  
pp. 44961-44971 ◽  
Author(s):  
Changlong Gu ◽  
Bo Liao ◽  
Xiaoying Li ◽  
Lijun Cai ◽  
Haowen Chen ◽  
...  
Keyword(s):  
Collaborative Filtering ◽  
Disease Association ◽  
Association Network ◽  
Recommendation Algorithm ◽  
Similarity Network ◽  
Disease Similarity

According to the miRNA and disease similarity network, the unknown associations are predicted by combining the known miRNA-disease association network based on collaborative filtering recommendation algorithm.

scholarly journals miRNA-Disease Association Prediction with Collaborative Matrix Factorization

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Zhen Shen ◽  
You-Hua Zhang ◽  
Kyungsook Han ◽  
Asoke K. Nandi ◽  
Barry Honig ◽  
...  
Keyword(s):  
Matrix Factorization ◽  
Noncoding Rna ◽  
Esophageal Neoplasms ◽  
Kidney Neoplasms ◽  
Disease Association ◽  
Computational Method ◽  
Experimental Identification ◽  
Novel Mirna ◽  
Disease Associations ◽  
High Prediction

As one of the factors in the noncoding RNA family, microRNAs (miRNAs) are involved in the development and progression of various complex diseases. Experimental identification of miRNA-disease association is expensive and time-consuming. Therefore, it is necessary to design efficient algorithms to identify novel miRNA-disease association. In this paper, we developed the computational method of Collaborative Matrix Factorization for miRNA-Disease Association prediction (CMFMDA) to identify potential miRNA-disease associations by integrating miRNA functional similarity, disease semantic similarity, and experimentally verified miRNA-disease associations. Experiments verified that CMFMDA achieves intended purpose and application values with its short consuming-time and high prediction accuracy. In addition, we used CMFMDA on Esophageal Neoplasms and Kidney Neoplasms to reveal their potential related miRNAs. As a result, 84% and 82% of top 50 predicted miRNA-disease pairs for these two diseases were confirmed by experiment. Not only this, but also CMFMDA could be applied to new diseases and new miRNAs without any known associations, which overcome the defects of many previous computational methods.

scholarly journals EOESGC: predicting miRNA-disease associations based on embedding of embedding and simplified graph convolutional network

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.

scholarly journals Prediction of lncRNA-disease association based on a Laplace normalized random walk with restart algorithm on heterogeneous networks

2022 ◽  
Vol 23 (1) ◽  
Author(s):  
Liugen Wang ◽  
Min Shang ◽  
Qi Dai ◽  
Ping-an He
Keyword(s):  
Random Walk ◽  
Heterogeneous Networks ◽  
Global Network ◽  
Random Walk With Restart ◽  
Lncrna Gene ◽  
Similarity Network ◽  
Disease Similarity ◽  
Disease Associations ◽  
Universal Network ◽  
Gene Similarity

Abstract Background More and more evidence showed that long non-coding RNAs (lncRNAs) play important roles in the development and progression of human sophisticated diseases. Therefore, predicting human lncRNA-disease associations is a challenging and urgently task in bioinformatics to research of human sophisticated diseases. Results In the work, a global network-based computational framework called as LRWRHLDA were proposed which is a universal network-based method. Firstly, four isomorphic networks include lncRNA similarity network, disease similarity network, gene similarity network and miRNA similarity network were constructed. And then, six heterogeneous networks include known lncRNA-disease, lncRNA-gene, lncRNA-miRNA, disease-gene, disease-miRNA, and gene-miRNA associations network were applied to design a multi-layer network. Finally, the Laplace normalized random walk with restart algorithm in this global network is suggested to predict the relationship between lncRNAs and diseases. Conclusions The ten-fold cross validation is used to evaluate the performance of LRWRHLDA. As a result, LRWRHLDA achieves an AUC of 0.98402, which is higher than other compared methods. Furthermore, LRWRHLDA can predict isolated disease-related lnRNA (isolated lnRNA related disease). The results for colorectal cancer, lung adenocarcinoma, stomach cancer and breast cancer have been verified by other researches. The case studies indicated that our method is effective.

A constrained probabilistic matrix decomposition method for predicting miRNA-disease associations

2020 ◽  
Vol 15 ◽  
Author(s):  
Xinguo Lu ◽  
Yan Gao ◽  
Zhenghao Zhu ◽  
Li Ding ◽  
Xinyu Wang ◽  
...  
Keyword(s):  
Matrix Decomposition ◽  
Disease Diagnosis ◽  
Similarity Network ◽  
Diagnosis And Prognosis ◽  
Non Coding Rna ◽  
Disease Similarity ◽  
Disease Associations ◽  
Novel Method ◽  
New Perspective ◽  
All Diseases

: MicroRNA is a type of non-coding RNA molecule whose length is about 22 nucleotides. The growing evidence shows that microRNA makes critical regulations in the development of complex diseases, such as cancers, cardiovascular diseases. Predicting potential microRNA-disease associations can provide a new perspective to achieve a better scheme of disease diagnosis and prognosis. However, there is a challenge to predict some potential essential microRNAs only with few known associations. To tackle this, we propose a novel method, named as constrained strategy for predicting microRNA-disease associations called CPMDA, in heterogeneous omics data. Here, we firstly construct disease similarity network and microRNA similarity network to preprocess the microRNAs with none available associations. Then, we apply probabilistic factorization to obtain two feature matrices of microRNA and disease. Meanwhile, we formulate a similarity feature matrix as constraints in the factorization process. Finally, we utilize obtained feature matrixes to identify potential associations for all diseases. The results indicate that CPMDA is superior over other methods in predicting potential microRNA-disease associations. Moreover, the evaluation show that CPMDA has a strong effect on microRNAs with few known associations. In case studies, CPMDA also demonstrated the effectiveness to infer unknown microRNAdisease associations for those novel diseases and microRNAs.

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.

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.

ES-MDA: Enhanced Similarity-based MiRNA-Disease Association

2020 ◽  
Vol 21 (11) ◽  
pp. 1060-1067
Author(s):  
Li Xu ◽  
Ge-Ning Jiang
Keyword(s):  
Machine Learning ◽  
Semantic Similarity ◽  
Prediction Accuracy ◽  
Experimental Validation ◽  
Computational Prediction ◽  
Disease Association ◽  
Biological Resource ◽  
Similarity Network ◽  
Machine Learning Methods ◽  
Disease Associations

: Accumulating evidence demonstrate that miRNAs can be treated as critical biomarkers in various complex human diseases. Thus, the identifications on potential miRNA-disease associations have become a hotpot for providing better understanding of disease pathology in this field. Recently, with various biological datasets, increasingly computational prediction approaches have been designed to uncover disease-related miRNAs for further experimental validation. To improve the prediction accuracy, several algorithms integrated miRNA similarities of known miRNA-disease associations to enhance the miRNA functional similarity network and disease similarities of known miRNA-disease associations to enhance the disease semantic similarity network. It is anticipated that machine learning methods would become an effective biological resource for clinical experimental guidance.

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