scholarly journals PWCDA: Path Weighted Method for Predicting circRNA-Disease Associations

2018 ◽  
Vol 19 (11) ◽  
pp. 3410 ◽  
Author(s):  
Xiujuan Lei ◽  
Zengqiang Fang ◽  
Luonan Chen ◽  
Fang-Xiang Wu
Keyword(s):  
Semantic Similarity ◽  
Heterogeneous Network ◽  
Functional Similarity ◽  
Computational Method ◽  
Related Gene ◽  
Similarity Network ◽  
Disease Pair ◽  
Disease Associations ◽  
Leave One Out ◽  
Similarity Scores

CircRNAs have particular biological structure and have proven to play important roles in diseases. It is time-consuming and costly to identify circRNA-disease associations by biological experiments. Therefore, it is appealing to develop computational methods for predicting circRNA-disease associations. In this study, we propose a new computational path weighted method for predicting circRNA-disease associations. Firstly, we calculate the functional similarity scores of diseases based on disease-related gene annotations and the semantic similarity scores of circRNAs based on circRNA-related gene ontology, respectively. To address missing similarity scores of diseases and circRNAs, we calculate the Gaussian Interaction Profile (GIP) kernel similarity scores for diseases and circRNAs, respectively, based on the circRNA-disease associations downloaded from circR2Disease database (http://bioinfo.snnu.edu.cn/CircR2Disease/). Then, we integrate disease functional similarity scores and circRNA semantic similarity scores with their related GIP kernel similarity scores to construct a heterogeneous network made up of three sub-networks: disease similarity network, circRNA similarity network and circRNA-disease association network. Finally, we compute an association score for each circRNA-disease pair based on paths connecting them in the heterogeneous network to determine whether this circRNA-disease pair is associated. We adopt leave one out cross validation (LOOCV) and five-fold cross validations to evaluate the performance of our proposed method. In addition, three common diseases, Breast Cancer, Gastric Cancer and Colorectal Cancer, are used for case studies. Experimental results illustrate the reliability and usefulness of our computational method in terms of different validation measures, which indicates PWCDA can effectively predict potential circRNA-disease associations.

scholarly journals Constructing lncRNA functional similarity network based on lncRNA-disease associations and disease semantic similarity

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Xing Chen ◽  
Chenggang Clarence Yan ◽  
Cai Luo ◽  
Wen Ji ◽  
Yongdong Zhang ◽  
...  
Keyword(s):  
Semantic Similarity ◽  
Functional Similarity ◽  
Similarity Network ◽  
Disease Associations

scholarly journals Genome-wide inferring gene–phenotype relationship by walking on the heterogeneous network

2010 ◽  
Vol 26 (9) ◽  
pp. 1219-1224 ◽  
Author(s):  
Yongjin Li ◽  
Jagdish C. Patra
Keyword(s):  
Heterogeneous Network ◽  
Gene Network ◽  
Genetic Diseases ◽  
Supplementary Information ◽  
Disease Genes ◽  
Phenotypic Data ◽  
Disease Associations ◽  
Improved Performance ◽  
Leave One Out ◽  
Phenotype Network

Abstract Motivation: Clinical diseases are characterized by distinct phenotypes. To identify disease genes is to elucidate the gene–phenotype relationships. Mutations in functionally related genes may result in similar phenotypes. It is reasonable to predict disease-causing genes by integrating phenotypic data and genomic data. Some genetic diseases are genetically or phenotypically similar. They may share the common pathogenetic mechanisms. Identifying the relationship between diseases will facilitate better understanding of the pathogenetic mechanism of diseases. Results: In this article, we constructed a heterogeneous network by connecting the gene network and phenotype network using the phenotype–gene relationship information from the OMIM database. We extended the random walk with restart algorithm to the heterogeneous network. The algorithm prioritizes the genes and phenotypes simultaneously. We use leave-one-out cross-validation to evaluate the ability of finding the gene–phenotype relationship. Results showed improved performance than previous works. We also used the algorithm to disclose hidden disease associations that cannot be found by gene network or phenotype network alone. We identified 18 hidden disease associations, most of which were supported by literature evidence. Availability: The MATLAB code of the program is available at http://www3.ntu.edu.sg/home/aspatra/research/Yongjin_BI2010.zip Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

scholarly journals Graph Convolutional Network and Convolutional Neural Network Based Method for Predicting lncRNA-Disease Associations

Cells ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 1012 ◽  
Author(s):  
Xuan ◽  
Pan ◽  
Zhang ◽  
Liu ◽  
Sun
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Heterogeneous Network ◽  
Heterogeneous Data ◽  
Superior Performance ◽  
Convolutional Network ◽  
Topological Information ◽  
Disease Pair ◽  
Disease Associations ◽  
The Right

Aberrant expressions of long non-coding RNAs (lncRNAs) are often associated with diseases and identification of disease-related lncRNAs is helpful for elucidating complex pathogenesis. Recent methods for predicting associations between lncRNAs and diseases integrate their pertinent heterogeneous data. However, they failed to deeply integrate topological information of heterogeneous network comprising lncRNAs, diseases, and miRNAs. We proposed a novel method based on the graph convolutional network and convolutional neural network, referred to as GCNLDA, to infer disease-related lncRNA candidates. The heterogeneous network containing the lncRNA, disease, and miRNA nodes, is constructed firstly. The embedding matrix of a lncRNA-disease node pair was constructed according to various biological premises about lncRNAs, diseases, and miRNAs. A new framework based on a graph convolutional network and a convolutional neural network was developed to learn network and local representations of the lncRNA-disease pair. On the left side of the framework, the autoencoder based on graph convolution deeply integrated topological information within the heterogeneous lncRNA-disease-miRNA network. Moreover, as different node features have discriminative contributions to the association prediction, an attention mechanism at node feature level is constructed. The left side learnt the network representation of the lncRNA-disease pair. The convolutional neural networks on the right side of the framework learnt the local representation of the lncRNA-disease pair by focusing on the similarities, associations, and interactions that are only related to the pair. Compared to several state-of-the-art prediction methods, GCNLDA had superior performance. Case studies on stomach cancer, osteosarcoma, and lung cancer confirmed that GCNLDA effectively discovers the potential lncRNA-disease associations.

Inferring novel lncRNA–disease associations based on a random walk model of a lncRNA functional similarity network

2014 ◽  
Vol 10 (8) ◽  
pp. 2074-2081 ◽  
Author(s):  
Jie Sun ◽  
Hongbo Shi ◽  
Zhenzhen Wang ◽  
Changjian Zhang ◽  
Lin Liu ◽  
...  
Keyword(s):  
Random Walk ◽  
Functional Similarity ◽  
Random Walk Model ◽  
Human Diseases ◽  
Similarity Network ◽  
Disease Associations ◽  
Non Coding Rnas

Accumulating evidence demonstrates that long non-coding RNAs (lncRNAs) play important roles in the development of complex human diseases. Predicting novel human lncRNA–disease associations is a challenging and essential task.

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 A Novel Approach for Predicting Disease-lncRNA Associations Based on the Distance Correlation Set and Information of the miRNAs

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Haochen Zhao ◽  
Linai Kuang ◽  
Lei Wang ◽  
Zhanwei Xuan
Keyword(s):  
Computational Models ◽  
State Of The Art ◽  
Experimental Studies ◽  
Research Field ◽  
Computational Method ◽  
Biological Processes ◽  
Distance Correlation ◽  
Novel Approach ◽  
Disease Associations ◽  
Leave One Out

Recently, accumulating laboratorial studies have indicated that plenty of long noncoding RNAs (lncRNAs) play important roles in various biological processes and are associated with many complex human diseases. Therefore, developing powerful computational models to predict correlation between lncRNAs and diseases based on heterogeneous biological datasets will be important. However, there are few approaches to calculating and analyzing lncRNA-disease associations on the basis of information about miRNAs. In this article, a new computational method based on distance correlation set is developed to predict lncRNA-disease associations (DCSLDA). Comparing with existing state-of-the-art methods, we found that the major novelty of DCSLDA lies in the introduction of lncRNA-miRNA-disease network and distance correlation set; thus DCSLDA can be applied to predict potential lncRNA-disease associations without requiring any known disease-lncRNA associations. Simulation results show that DCSLDA can significantly improve previous existing models with reliable AUC of 0.8517 in the leave-one-out cross-validation. Furthermore, while implementing DCSLDA to prioritize candidate lncRNAs for three important cancers, in the first 0.5% of forecast results, 17 predicted associations are verified by other independent studies and biological experimental studies. Hence, it is anticipated that DCSLDA could be a great addition to the biomedical research field.

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 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.

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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