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

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.

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A network embedding-based multiple information integration method for the MiRNA-disease association prediction

BMC Bioinformatics ◽

10.1186/s12859-019-3063-3 ◽

2019 ◽

Vol 20 (1) ◽

Cited By ~ 17

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.

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EHAI: Enhanced Human Microbe-Disease Association Identification

Current Protein and Peptide Science ◽

10.2174/1389203721666200702150249 ◽

2020 ◽

Vol 21 (11) ◽

pp. 1078-1084

Author(s):

Ruizhi Fan ◽

Chenhua Dong ◽

Hu Song ◽

Yixin Xu ◽

Linsen Shi ◽

...

Keyword(s):

Microbial Community ◽

Human Health ◽

Complex Diseases ◽

Disease Association ◽

Computational Results ◽

Computational Approaches ◽

Disease Diagnostics ◽

Disease Associations ◽

Association Discovery ◽

Biological Tool

: Recently, an increasing number of biological and clinical reports have demonstrated that imbalance of microbial community has the ability to play important roles among several complex diseases concerning human health. Having a good knowledge of discovering potential of microbe-disease relationships, which provides the ability to having a better understanding of some issues, including disease pathology, further boosts disease diagnostics and prognostics, has been taken into account. Nevertheless, a few computational approaches can meet the need of huge scale of microbe-disease association discovery. In this work, we proposed the EHAI model, which is Enhanced Human microbe- disease Association Identification. EHAI employed the microbe-disease associations, and then Gaussian interaction profile kernel similarity has been utilized to enhance the basic microbe-disease association. Actually, some known microbe-disease associations and a large amount of associations are still unavailable among the datasets. The ‘super-microbe’ and ‘super-disease’ were employed to enhance the model. Computational results demonstrated that such super-classes have the ability to be helpful to the performance of EHAI. Therefore, it is anticipated that EHAI can be treated as an important biological tool in this field.

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Multiple Linear Regression Analysis of lncRNA–Disease Association Prediction Based on Clinical Prognosis Data

BioMed Research International ◽

10.1155/2018/3823082 ◽

2018 ◽

Vol 2018 ◽

pp. 1-10 ◽

Cited By ~ 2

Author(s):

Bo Wang ◽

Jing Zhang

Keyword(s):

Prostate Cancer ◽

Linear Regression ◽

Multiple Linear Regression ◽

Cancer Survival ◽

Disease Association ◽

The Body ◽

Survival Prediction ◽

Clinical Prognosis ◽

Disease Associations ◽

Auc Value

Long noncoding RNAs (lncRNAs) have an important role in various life processes of the body, especially cancer. The analysis of disease prognosis is ignored in current prediction on lncRNA–disease associations. In this study, a multiple linear regression model was constructed for lncRNA–disease association prediction based on clinical prognosis data (MlrLDAcp), which integrated the cancer data of clinical prognosis and the expression quantity of lncRNA transcript. MlrLDAcp could realize not only cancer survival prediction but also lncRNA–disease association prediction. Ultimately, 60 lncRNAs most closely related to prostate cancer survival were selected from 481 alternative lncRNAs. Then, the multiple linear regression relationship between the prognosis survival of 176 patients with prostate cancer and 60 lncRNAs was also given. Compared with previous studies, MlrLDAcp had a predominant survival predictive ability and could effectively predict lncRNA–disease associations. MlrLDAcp had an area under the curve (AUC) value of 0.875 for survival prediction and an AUC value of 0.872 for lncRNA–disease association prediction. It could be an effective biological method for biomedical research.

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

Current Bioinformatics ◽

10.2174/1574893616999210120181506 ◽

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.

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Exploring Disease Association from the NHANES Data

Exploring Advances in Interdisciplinary Data Mining and Analytics ◽

10.4018/978-1-61350-474-1.ch010 ◽

2011 ◽

pp. 157-173

Author(s):

Zhengzheng Xing ◽

Jian Pei

Keyword(s):

Pattern Mining ◽

Disease Association ◽

Medical Data ◽

Mining Method ◽

Medical Data Mining ◽

Data Set ◽

Hierarchical Tree ◽

Disease Associations ◽

Disease Pattern ◽

Influence Graph

Finding associations among different diseases is an important task in medical data mining. The NHANES data is a valuable source in exploring disease associations. However, existing studies analyzing the NHANES data focus on using statistical techniques to test a small number of hypotheses. This NHANES data has not been systematically explored for mining disease association patterns. In this regard, this paper proposes a direct disease pattern mining method and an interactive disease pattern mining method to explore the NHANES data. The results on the latest NHANES data demonstrate that these methods can mine meaningful disease associations consistent with the existing knowledge and literatures. Furthermore, this study provides summarization of the data set via a disease influence graph and a disease hierarchical tree.

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DNILMF-LDA: Prediction of lncRNA-Disease Associations by Dual-Network Integrated Logistic Matrix Factorization and Bayesian Optimization

Genes ◽

10.3390/genes10080608 ◽

2019 ◽

Vol 10 (8) ◽

pp. 608 ◽

Cited By ~ 3

Author(s):

Yan Li ◽

Junyi Li ◽

Naizheng Bian

Keyword(s):

Matrix Factorization ◽

Disease Diagnosis ◽

Disease Association ◽

Bayesian Optimization ◽

Model Parameters ◽

Target Interaction ◽

Disease Associations ◽

Auc Value ◽

Similarity Networks ◽

Fold Cross Validation

Identifying associations between lncRNAs and diseases can help understand disease-related lncRNAs and facilitate disease diagnosis and treatment. The dual-network integrated logistic matrix factorization (DNILMF) model has been used for drug–target interaction prediction, and good results have been achieved. We firstly applied DNILMF to lncRNA–disease association prediction (DNILMF-LDA). We combined different similarity kernel matrices of lncRNAs and diseases by using nonlinear fusion to extract the most important information in fused matrices. Then, lncRNA–disease association networks and similarity networks were built simultaneously. Finally, the Gaussian process mutual information (GP-MI) algorithm of Bayesian optimization was adopted to optimize the model parameters. The 10-fold cross-validation result showed that the area under receiving operating characteristic (ROC) curve (AUC) value of DNILMF-LDA was 0.9202, and the area under precision-recall (PR) curve (AUPR) was 0.5610. Compared with LRLSLDA, SIMCLDA, BiwalkLDA, and TPGLDA, the AUC value of our method increased by 38.81%, 13.07%, 8.35%, and 6.75%, respectively. The AUPR value of our method increased by 52.66%, 40.05%, 37.01%, and 44.25%. These results indicate that DNILMF-LDA is an effective method for predicting the associations between lncRNAs and diseases.

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Computational drug repositioning based on multi-similarities bilinear matrix factorization

Briefings in Bioinformatics ◽

10.1093/bib/bbaa267 ◽

2020 ◽

Author(s):

Mengyun Yang ◽

Gaoyan Wu ◽

Qichang Zhao ◽

Yaohang Li ◽

Jianxin Wang

Keyword(s):

Matrix Factorization ◽

Drug Repositioning ◽

Disease Association ◽

Biological Entity ◽

Biomedical Data ◽

Supplementary Data ◽

Practical Applications ◽

Disease Associations ◽

Association Matrix ◽

Similarity Matrices

Abstract With the development of high-throughput technology and the accumulation of biomedical data, the prior information of biological entity can be calculated from different aspects. Specifically, drug–drug similarities can be measured from target profiles, drug–drug interaction and side effects. Similarly, different methods and data sources to calculate disease ontology can result in multiple measures of pairwise disease similarities. Therefore, in computational drug repositioning, developing a dynamic method to optimize the fusion process of multiple similarities is a crucial and challenging task. In this study, we propose a multi-similarities bilinear matrix factorization (MSBMF) method to predict promising drug-associated indications for existing and novel drugs. Instead of fusing multiple similarities into a single similarity matrix, we concatenate these similarity matrices of drug and disease, respectively. Applying matrix factorization methods, we decompose the drug–disease association matrix into a drug-feature matrix and a disease-feature matrix. At the same time, using these feature matrices as basis, we extract effective latent features representing the drug and disease similarity matrices to infer missing drug–disease associations. Moreover, these two factored matrices are constrained by non-negative factorization to ensure that the completed drug–disease association matrix is biologically interpretable. In addition, we numerically solve the MSBMF model by an efficient alternating direction method of multipliers algorithm. The computational experiment results show that MSBMF obtains higher prediction accuracy than the state-of-the-art drug repositioning methods in cross-validation experiments. Case studies also demonstrate the effectiveness of our proposed method in practical applications. Availability: The data and code of MSBMF are freely available at https://github.com/BioinformaticsCSU/MSBMF. Corresponding author: Jianxin Wang, School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, P. R. China. E-mail: [email protected] Supplementary Data: Supplementary data are available online at https://academic.oup.com/bib.

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miRNA-Disease Association Prediction with Collaborative Matrix Factorization

Complexity ◽

10.1155/2017/2498957 ◽

2017 ◽

Vol 2017 ◽

pp. 1-9 ◽

Cited By ~ 29

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.

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

BMC Bioinformatics ◽

10.1186/s12859-019-3066-0 ◽

2019 ◽

Vol 20 (1) ◽

Cited By ~ 4

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.

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