Integrating Bipartite Network Projection and KATZ Measure to Identify Novel CircRNA-Disease Associations

2019 ◽  
Vol 18 (4) ◽  
pp. 578-584 ◽  
Author(s):  
Qi Zhao ◽  
Yingjuan Yang ◽  
Guofei Ren ◽  
Erxia Ge ◽  
Chunlong Fan
Keyword(s):  
Bipartite Network ◽  
Disease Associations

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.

Predicting drug-disease associations and their therapeutic function based on the drug-disease association bipartite network

Methods ◽  
2018 ◽  
Vol 145 ◽  
pp. 51-59 ◽  
Author(s):  
Wen Zhang ◽  
Xiang Yue ◽  
Feng Huang ◽  
Ruoqi Liu ◽  
Yanlin Chen ◽  
...  
Keyword(s):  
Disease Association ◽  
Bipartite Network ◽  
Therapeutic Function ◽  
Disease Associations

scholarly journals WMGHMDA: a novel weighted meta-graph-based model for predicting human microbe-disease association on heterogeneous information network

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Yahui Long ◽  
Jiawei Luo
Keyword(s):  
Computational Methods ◽  
Cross Validation ◽  
Search Algorithm ◽  
Human Diseases ◽  
Information Network ◽  
Bipartite Network ◽  
Heterogeneous Information Network ◽  
Similarity Network ◽  
Heterogeneous Information ◽  
Disease Associations

Abstract Background An increasing number of biological and clinical evidences have indicated that the microorganisms significantly get involved in the pathological mechanism of extensive varieties of complex human diseases. Inferring potential related microbes for diseases can not only promote disease prevention, diagnosis and treatment, but also provide valuable information for drug development. Considering that experimental methods are expensive and time-consuming, developing computational methods is an alternative choice. However, most of existing methods are biased towards well-characterized diseases and microbes. Furthermore, existing computational methods are limited in predicting potential microbes for new diseases. Results Here, we developed a novel computational model to predict potential human microbe-disease associations (MDAs) based on Weighted Meta-Graph (WMGHMDA). We first constructed a heterogeneous information network (HIN) by combining the integrated microbe similarity network, the integrated disease similarity network and the known microbe-disease bipartite network. And then, we implemented iteratively pre-designed Weighted Meta-Graph search algorithm on the HIN to uncover possible microbe-disease pairs by cumulating the contribution values of weighted meta-graphs to the pairs as their probability scores. Depending on contribution potential, we described the contribution degree of different types of meta-graphs to a microbe-disease pair with bias rating. Meta-graph with higher bias rating will be assigned greater weight value when calculating probability scores. Conclusions The experimental results showed that WMGHMDA outperformed some state-of-the-art methods with average AUCs of 0.9288, 0.9068 ±0.0031 in global leave-one-out cross validation (LOOCV) and 5-fold cross validation (5-fold CV), respectively. In the case studies, 9, 19, 37 and 10, 20, 45 out of top-10, 20, 50 candidate microbes were manually verified by previous reports for asthma and inflammatory bowel disease (IBD), respectively. Furthermore, three common human diseases (Crohn’s disease, Liver cirrhosis, Type 1 diabetes) were adopted to demonstrate that WMGHMDA could be efficiently applied to make predictions for new diseases. In summary, WMGHMDA has a high potential in predicting microbe-disease associations.

scholarly journals Predicting Metabolite-Disease Associations Based on Linear Neighborhood Similarity with Improved Bipartite Network Projection Algorithm

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Xiujuan Lei ◽  
Cheng Zhang
Keyword(s):  
Projection Method ◽  
Prediction Method ◽  
Human Diseases ◽  
Projection Algorithm ◽  
Bipartite Network ◽  
Computational Tool ◽  
Clinical Observations ◽  
Disease Associations ◽  
Reliable Performance ◽  
New Feature

A large number of clinical observations have showed that metabolites are involved in a variety of important human diseases in the recent years. Nonetheless, the inherent noise and incompleteness in the existing biological datasets are tough factors which limit the prediction accuracy of current computational methods. To solve this problem, in this paper, a prediction method, IBNPLNSMDA, is proposed which uses the improved bipartite network projection method to predict latent metabolite-disease associations based on linear neighborhood similarity. Specifically, liner neighborhood similarity matrix about metabolites (diseases) is reconstructed according to the new feature which is gained by the known metabolite-disease associations and relevant integrated similarities. The improved bipartite network projection method is adopted to infer the potential associations between metabolites and diseases. At last, IBNPLNSMDA achieves a reliable performance in LOOCV (AUC of 0.9634) outperforming the compared methods. In addition, in case studies of four common human diseases, simulation results confirm the utility of our method in discovering latent metabolite-disease pairs. Thus, we believe that IBNPLNSMDA could serve as a reliable computational tool for metabolite-disease associations prediction.

A Novel Approach based on Bipartite Network to Predict Human Microbe-Disease Associations

2018 ◽  
Vol 13 (2) ◽  
pp. 141-148 ◽  
Author(s):  
Lei Wang ◽  
Pengyao Ping ◽  
Linai Kuang ◽  
Songtao Ye ◽  
Faisal Muhammad Buland lqbal ◽  
...  
Keyword(s):  
Bipartite Network ◽  
Novel Approach ◽  
Disease Associations

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.

scholarly journals Identifying potential association on gene-disease network via dual hypergraph regularized least squares

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Hongpeng Yang ◽  
Yijie Ding ◽  
Jijun Tang ◽  
Fei Guo
Keyword(s):  
Least Squares ◽  
Research Work ◽  
Complex Diseases ◽  
Higher Order ◽  
Biological Data ◽  
Biological Information ◽  
Bipartite Network ◽  
Regularized Least Squares ◽  
Verification Methods ◽  
Disease Associations

Abstract Background Identifying potential associations between genes and diseases via biomedical experiments must be the time-consuming and expensive research works. The computational technologies based on machine learning models have been widely utilized to explore genetic information related to complex diseases. Importantly, the gene-disease association detection can be defined as the link prediction problem in bipartite network. However, many existing methods do not utilize multiple sources of biological information; Additionally, they do not extract higher-order relationships among genes and diseases. Results In this study, we propose a novel method called Dual Hypergraph Regularized Least Squares (DHRLS) with Centered Kernel Alignment-based Multiple Kernel Learning (CKA-MKL), in order to detect all potential gene-disease associations. First, we construct multiple kernels based on various biological data sources in gene and disease spaces respectively. After that, we use CAK-MKL to obtain the optimal kernels in the two spaces respectively. To specific, hypergraph can be employed to establish higher-order relationships. Finally, our DHRLS model is solved by the Alternating Least squares algorithm (ALSA), for predicting gene-disease associations. Conclusion Comparing with many outstanding prediction tools, DHRLS achieves best performance on gene-disease associations network under two types of cross validation. To verify robustness, our proposed approach has excellent prediction performance on six real-world networks. Our research work can effectively discover potential disease-associated genes and provide guidance for the follow-up verification methods of complex diseases.

scholarly journals Network-Based Inference Methods for Drug Repositioning

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Hailin Chen ◽  
Heng Zhang ◽  
Zuping Zhang ◽  
Yiqin Cao ◽  
Wenliang Tang
Keyword(s):  
Network Topology ◽  
Drug Repositioning ◽  
Biological Information ◽  
Bipartite Network ◽  
Reliable Prediction ◽  
Comparative Toxicogenomics Database ◽  
Disease Associations ◽  
Speed Up ◽  
Basic Network ◽  
Inference Methods

Mining potential drug-disease associations can speed up drug repositioning for pharmaceutical companies. Previous computational strategies focused on prior biological information for association inference. However, such information may not be comprehensively available and may contain errors. Different from previous research, two inference methods,ProbSandHeatS, were introduced in this paper to predict direct drug-disease associations based only on the basic network topology measure. Bipartite network topology was used to prioritize the potentially indicated diseases for a drug. Experimental results showed that both methods can receive reliable prediction performance and achieve AUC values of 0.9192 and 0.9079, respectively. Case studies on real drugs indicated that some of the strongly predicted associations were confirmed by results in the Comparative Toxicogenomics Database (CTD). Finally, a comprehensive prediction of drug-disease associations enables us to suggest many new drug indications for further studies.

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