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.

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.

MDAPlatform: a Component-based Platform for Constructing and Assessing miRNA-disease association Prediction Methods

2021 ◽  
Vol 16 ◽  
Author(s):  
Yayan Zhang ◽  
Guihua Duan ◽  
Cheng Yan ◽  
Haolun Yi ◽  
Fang-Xiang Wu ◽  
...  
Keyword(s):  
Computational Models ◽  
Critical Role ◽  
Prediction Method ◽  
Disease Association ◽  
Prediction Methods ◽  
Comparison Results ◽  
Disease Associations ◽  
Pros And Cons ◽  
Clinical Drugs ◽  
Validation Experiments

Background: Increasing evidence has indicated that miRNA-disease association prediction plays a critical role in the study of clinical drugs. Researchers have proposed many computational models for miRNA-disease prediction. However, there is no unified platform to compare and analyze the pros and cons or share the code and data of these models. Objective: In this study, we develop an easy-to-use platform (MDAPlatform) to construct and assess miRNA-disease association prediction method. Methods: MDAPlatform integrates the relevant data of miRNA, disease and miRNA-disease associations that are used in previous miRNA-disease association prediction studies. Based on the componentized model, it develops differet components of previous computational methods. Results: Users can conduct cross validation experiments and compare their methods with other methods, and the visualized comparison results are also provided. Conclusion: Based on the componentized model, MDAPlatform provides easy-to-operate interfaces to construct the miRNA-disease association method, which is beneficial to develop new miRNA-disease association prediction methods in the future.

scholarly journals LncTarD: a manually-curated database of experimentally-supported functional lncRNA–target regulations in human diseases

2019 ◽  
Author(s):  
Hongying Zhao ◽  
Jian Shi ◽  
Yunpeng Zhang ◽  
Aimin Xie ◽  
Lei Yu ◽  
...  
Keyword(s):  
Molecular Mechanisms ◽  
Sequence Data ◽  
Human Diseases ◽  
Regulatory Mechanisms ◽  
Biological Functions ◽  
Functional Dynamics ◽  
Disease Associations ◽  
Non Coding Rnas ◽  
User Friendly ◽  
Pan Cancer

Abstract Long non-coding RNAs (lncRNAs) are associated with human diseases. Although lncRNA–disease associations have received significant attention, no online repository is available to collect lncRNA-mediated regulatory mechanisms, key downstream targets, and important biological functions driven by disease-related lncRNAs in human diseases. We thus developed LncTarD (http://biocc.hrbmu.edu.cn/LncTarD/ or http://bio-bigdata.hrbmu.edu.cn/LncTarD), a manually-curated database that provides a comprehensive resource of key lncRNA–target regulations, lncRNA-influenced functions, and lncRNA-mediated regulatory mechanisms in human diseases. LncTarD offers (i) 2822 key lncRNA–target regulations involving 475 lncRNAs and 1039 targets associated with 177 human diseases; (ii) 1613 experimentally-supported functional regulations and 1209 expression associations in human diseases; (iii) important biological functions driven by disease-related lncRNAs in human diseases; (iv) lncRNA–target regulations responsible for drug resistance or sensitivity in human diseases and (v) lncRNA microarray, lncRNA sequence data and transcriptome data of an 11 373 pan-cancer patient cohort from TCGA to help characterize the functional dynamics of these lncRNA–target regulations. LncTarD also provides a user-friendly interface to conveniently browse, search, and download data. LncTarD will be a useful resource platform for the further understanding of functions and molecular mechanisms of lncRNA deregulation in human disease, which will help to identify novel and sensitive biomarkers and therapeutic targets.

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

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 A Regularized Gradient Projection Method for the Minimization Problem

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
Yonghong Yao ◽  
Shin Min Kang ◽  
Wu Jigang ◽  
Pei-Xia Yang
Keyword(s):  
Projection Method ◽  
Minimization Problem ◽  
Gradient Projection ◽  
Projection Algorithm ◽  
Gradient Projection Method ◽  
Minimum Norm ◽  
Minimum Norm Solution ◽  
Gradient Projection Algorithm

We investigate the following regularized gradient projection algorithmxn+1=Pc(I−γn(∇f+αnI))xn,n≥0. Under some different control conditions, we prove that this gradient projection algorithm strongly converges to the minimum norm solution of the minimization problemminx∈Cf(x).

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 Inferring miRNA-disease associations using collaborative filtering and resource allocation on a tripartite graph

2021 ◽  
Vol 14 (S3) ◽  
Author(s):  
Van Tinh Nguyen ◽  
Thi Tu Kien Le ◽  
Tran Quoc Vinh Nguyen ◽  
Dang Hung Tran
Keyword(s):  
Resource Allocation ◽  
Collaborative Filtering ◽  
Open Angle Glaucoma ◽  
Imbalanced Data ◽  
New Associations ◽  
Tripartite Graph ◽  
Computer Scientists ◽  
Disease Associations ◽  
Reliable Performance ◽  
Top 40

Abstract Background Developing efficient and successful computational methods to infer potential miRNA-disease associations is urgently needed and is attracting many computer scientists in recent years. The reason is that miRNAs are involved in many important biological processes and it is tremendously expensive and time-consuming to do biological experiments to verify miRNA-disease associations. Methods In this paper, we proposed a new method to infer miRNA-disease associations using collaborative filtering and resource allocation algorithms on a miRNA-disease-lncRNA tripartite graph. It combined the collaborative filtering algorithm in CFNBC model to solve the problem of imbalanced data and the method for association prediction established multiple types of known associations among multiple objects presented in TPGLDA model. Results The experimental results showed that our proposed method achieved a reliable performance with Area Under Roc Curve (AUC) and Area Under Precision-Recall Curve (AUPR) values of 0.9788 and 0.9373, respectively, under fivefold-cross-validation experiments. It outperformed than some other previous methods such as DCSMDA and TPGLDA. Furthermore, it demonstrated the ability to derive new associations between miRNAs and diseases among 8, 19 and 14 new associations out of top 40 predicted associations in case studies of Prostatic Neoplasms, Heart Failure, and Glioma diseases, respectively. All of these new predicted associations have been confirmed by recent literatures. Besides, it could discover new associations for new diseases (or miRNAs) without any known associations as demonstrated in the case study of Open-angle glaucoma disease. Conclusion With the reliable performance to infer new associations between miRNAs and diseases as well as to discover new associations for new diseases (or miRNAs) without any known associations, our proposed method can be considered as a powerful tool to infer miRNA-disease associations.

scholarly journals Bipartite graph-based collaborative matrix factorization method for predicting miRNA-disease associations

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Feng Zhou ◽  
Meng-Meng Yin ◽  
Cui-Na Jiao ◽  
Zhen Cui ◽  
Jing-Xiu Zhao ◽  
...  
Keyword(s):  
Bipartite Graph ◽  
Matrix Factorization ◽  
Cross Validation ◽  
Rapid Development ◽  
Factorization Method ◽  
Computational Method ◽  
Human Diseases ◽  
Simulation Experiments ◽  
Disease Associations ◽  
Fold Cross Validation

Abstract Background With the rapid development of various advanced biotechnologies, researchers in related fields have realized that microRNAs (miRNAs) play critical roles in many serious human diseases. However, experimental identification of new miRNA–disease associations (MDAs) is expensive and time-consuming. Practitioners have shown growing interest in methods for predicting potential MDAs. In recent years, an increasing number of computational methods for predicting novel MDAs have been developed, making a huge contribution to the research of human diseases and saving considerable time. In this paper, we proposed an efficient computational method, named bipartite graph-based collaborative matrix factorization (BGCMF), which is highly advantageous for predicting novel MDAs. Results By combining two improved recommendation methods, a new model for predicting MDAs is generated. Based on the idea that some new miRNAs and diseases do not have any associations, we adopt the bipartite graph based on the collaborative matrix factorization method to complete the prediction. The BGCMF achieves a desirable result, with AUC of up to 0.9514 ± (0.0007) in the five-fold cross-validation experiments. Conclusions Five-fold cross-validation is used to evaluate the capabilities of our method. Simulation experiments are implemented to predict new MDAs. More importantly, the AUC value of our method is higher than those of some state-of-the-art methods. Finally, many associations between new miRNAs and new diseases are successfully predicted by performing simulation experiments, indicating that BGCMF is a useful method to predict more potential miRNAs with roles in various diseases.

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