scholarly journals Predicting microRNA-disease association based on microRNA structural and functional similarity network

2019 ◽  
Vol 7 (2) ◽  
pp. 138-146
Author(s):  
Tao Ding ◽  
Jie Gao ◽  
Shanshan Zhu ◽  
Junhua Xu ◽  
Min Wu
Keyword(s):  
Disease Association ◽  
Functional Similarity ◽  
Similarity Network

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.

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 Prioritization of candidate cancer drugs based on a drug functional similarity network constructed by integrating pathway activities and drug activities

2019 ◽  
Vol 13 (10) ◽  
pp. 2259-2277 ◽  
Author(s):  
Jieyi Di ◽  
Baotong Zheng ◽  
Qingfei Kong ◽  
Ying Jiang ◽  
Siyao Liu ◽  
...  
Keyword(s):  
Functional Similarity ◽  
Cancer Drugs ◽  
Similarity Network ◽  
Drug Activities

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.

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 Binary Interactome Models of Inner- Versus Outer-Complexome Organization

2021 ◽  
Author(s):  
Luke Lambourne ◽  
Anupama Yadav ◽  
Yang Wang ◽  
Alice Desbuleux ◽  
Dae-Kyum Kim ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Small Proportion ◽  
Cellular Systems ◽  
Functional Similarity ◽  
Functional Heterogeneity ◽  
Protein Protein Interactions ◽  
Functional Plasticity ◽  
Similarity Network ◽  
Transient Interactions ◽  
Similarity Relationships

The interactome is often conceived of as a collection of hundreds of multimeric machines, collectively the "complexome". However, the large proportion of the proteome that exists outside of the complexome, (the "outer-complexome") is expected to account for most of the functional plasticity exhibited by cellular systems. To compare features of inner- versus outer-complexome organization, we generated an all-by-all yeast systematic binary interactome map, integrated it with previous binary maps, and compared the resulting binary interactome "atlas" with systematic co-complex association and functional similarity network maps. Direct binary protein-protein interactions in the inner-complexome tend to be readily detected in different assays and exhibit high levels of coherence with functional similarity relationships. In contrast, pairs of proteins connected by relatively transient, harder to detect binary interactions in the outer-complexome appear to exhibit higher levels of functional heterogeneity. Thus a small proportion of the interactome corresponds to a stable, functionally homogeneous inner-complexome, while a much greater proportion consists of mostly transient interactions between pairs of functionally heterogeneous proteins in the outer-complexome.

scholarly journals Refine gene functional similarity network based on interaction networks

2017 ◽  
Vol 18 (S16) ◽  
Author(s):  
Zhen Tian ◽  
Maozu Guo ◽  
Chunyu Wang ◽  
Xiaoyan Liu ◽  
Shiming Wang
Keyword(s):  
Functional Similarity ◽  
Interaction Networks ◽  
Similarity Network

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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