scholarly journals SCMFMDA: Predicting microRNA-disease associations based on similarity constrained matrix factorization

2021 ◽  
Vol 17 (7) ◽  
pp. e1009165
Author(s):  
Lei Li ◽  
Zhen Gao ◽  
Yu-Tian Wang ◽  
Ming-Wen Zhang ◽  
Jian-Cheng Ni ◽  
...  
Keyword(s):  
Matrix Factorization ◽  
Cross Validation ◽  
Sequence Similarity ◽  
Nonnegative Matrix ◽  
Functional Similarity ◽  
Human Diseases ◽  
Mirna Sequence ◽  
Fusion Algorithm ◽  
Disease Associations ◽  
Leave One Out

miRNAs belong to small non-coding RNAs that are related to a number of complicated biological processes. Considerable studies have suggested that miRNAs are closely associated with many human diseases. In this study, we proposed a computational model based on Similarity Constrained Matrix Factorization for miRNA-Disease Association Prediction (SCMFMDA). In order to effectively combine different disease and miRNA similarity data, we applied similarity network fusion algorithm to obtain integrated disease similarity (composed of disease functional similarity, disease semantic similarity and disease Gaussian interaction profile kernel similarity) and integrated miRNA similarity (composed of miRNA functional similarity, miRNA sequence similarity and miRNA Gaussian interaction profile kernel similarity). In addition, the L2 regularization terms and similarity constraint terms were added to traditional Nonnegative Matrix Factorization algorithm to predict disease-related miRNAs. SCMFMDA achieved AUCs of 0.9675 and 0.9447 based on global Leave-one-out cross validation and five-fold cross validation, respectively. Furthermore, the case studies on two common human diseases were also implemented to demonstrate the prediction accuracy of SCMFMDA. The out of top 50 predicted miRNAs confirmed by experimental reports that indicated SCMFMDA was effective for prediction of relationship between miRNAs and diseases.

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.

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 Multi-Similarities Bilinear Matrix Factorization-Based Method for Predicting Human Microbe–Disease Associations

2021 ◽  
Vol 12 ◽  
Author(s):  
Xiaoyu Yang ◽  
Linai Kuang ◽  
Zhiping Chen ◽  
Lei Wang
Keyword(s):  
Matrix Factorization ◽  
Cross Validation ◽  
Cosine Similarity ◽  
Disease Associations ◽  
Novel Method ◽  
Inflammatory Bowel ◽  
Leave One Out ◽  
Association Matrix ◽  
Similarity Matrices

Accumulating studies have shown that microbes are closely related to human diseases. In this paper, a novel method called MSBMFHMDA was designed to predict potential microbe–disease associations by adopting multi-similarities bilinear matrix factorization. In MSBMFHMDA, a microbe multiple similarities matrix was constructed first based on the Gaussian interaction profile kernel similarity and cosine similarity for microbes. Then, we use the Gaussian interaction profile kernel similarity, cosine similarity, and symptom similarity for diseases to compose the disease multiple similarities matrix. Finally, we integrate these two similarity matrices and the microbe-disease association matrix into our model to predict potential associations. The results indicate that our method can achieve reliable AUCs of 0.9186 and 0.9043 ± 0.0048 in the framework of leave-one-out cross validation (LOOCV) and fivefold cross validation, respectively. What is more, experimental results indicated that there are 10, 10, and 8 out of the top 10 related microbes for asthma, inflammatory bowel disease, and type 2 diabetes mellitus, respectively, which were confirmed by experiments and literatures. Therefore, our model has favorable performance in predicting potential microbe–disease associations.

Adaptive boosting-based computational model for predicting potential miRNA-disease associations

2019 ◽  
Vol 35 (22) ◽  
pp. 4730-4738 ◽  
Author(s):  
Yan Zhao ◽  
Xing Chen ◽  
Jun Yin
Keyword(s):  
Computational Models ◽  
Cross Validation ◽  
Learning Algorithm ◽  
Area Under The Curve ◽  
Human Diseases ◽  
Supplementary Information ◽  
Weak Classifier ◽  
Adaptive Boosting ◽  
Disease Associations ◽  
Leave One Out

AbstractMotivationRecent studies have shown that microRNAs (miRNAs) play a critical part in several biological processes and dysregulation of miRNAs is related with numerous complex human diseases. Thus, in-depth research of miRNAs and their association with human diseases can help us to solve many problems.ResultsDue to the high cost of traditional experimental methods, revealing disease-related miRNAs through computational models is a more economical and efficient way. Considering the disadvantages of previous models, in this paper, we developed adaptive boosting for miRNA-disease association prediction (ABMDA) to predict potential associations between diseases and miRNAs. We balanced the positive and negative samples by performing random sampling based on k-means clustering on negative samples, whose process was quick and easy, and our model had higher efficiency and scalability for large datasets than previous methods. As a boosting technology, ABMDA was able to improve the accuracy of given learning algorithm by integrating weak classifiers that could score samples to form a strong classifier based on corresponding weights. Here, we used decision tree as our weak classifier. As a result, the area under the curve (AUC) of global and local leave-one-out cross validation reached 0.9170 and 0.8220, respectively. What is more, the mean and the standard deviation of AUCs achieved 0.9023 and 0.0016, respectively in 5-fold cross validation. Besides, in the case studies of three important human cancers, 49, 50 and 50 out of the top 50 predicted miRNAs for colon neoplasms, hepatocellular carcinoma and breast neoplasms were confirmed by the databases and experimental literatures.Availability and implementationThe code and dataset of ABMDA are freely available at https://github.com/githubcode007/ABMDA.Supplementary informationSupplementary data are available at Bioinformatics online.

scholarly journals SRMDAP: SimRank and Density-Based Clustering Recommender Model for miRNA-Disease Association Prediction

2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
Xiaoying Li ◽  
Yaping Lin ◽  
Changlong Gu ◽  
Zejun Li
Keyword(s):  
Cross Validation ◽  
Computational Method ◽  
Human Diseases ◽  
Excellent Performance ◽  
Aberrant Expression ◽  
Experimental Identification ◽  
Density Based Clustering ◽  
Disease Associations ◽  
Leave One Out ◽  
The Relationship

Aberrant expression of microRNAs (miRNAs) can be applied for the diagnosis, prognosis, and treatment of human diseases. Identifying the relationship between miRNA and human disease is important to further investigate the pathogenesis of human diseases. However, experimental identification of the associations between diseases and miRNAs is time-consuming and expensive. Computational methods are efficient approaches to determine the potential associations between diseases and miRNAs. This paper presents a new computational method based on the SimRank and density-based clustering recommender model for miRNA-disease associations prediction (SRMDAP). The AUC of 0.8838 based on leave-one-out cross-validation and case studies suggested the excellent performance of the SRMDAP in predicting miRNA-disease associations. SRMDAP could also predict diseases without any related miRNAs and miRNAs without any related diseases.

scholarly journals IILLS: predicting virus-receptor interactions based on similarity and semi-supervised learning

2019 ◽  
Vol 20 (S23) ◽  
Author(s):  
Cheng Yan ◽  
Guihua Duan ◽  
Fang-Xiang Wu ◽  
Jianxin Wang
Keyword(s):  
Infectious Diseases ◽  
Cross Validation ◽  
Sequence Similarity ◽  
Least Square ◽  
Computational Method ◽  
Receptor Interaction ◽  
Virus Receptor ◽  
Receptor Interactions ◽  
Leave One Out ◽  
Fold Cross Validation

Abstract Background Viral infectious diseases are the serious threat for human health. The receptor-binding is the first step for the viral infection of hosts. To more effectively treat human viral infectious diseases, the hidden virus-receptor interactions must be discovered. However, current computational methods for predicting virus-receptor interactions are limited. Result In this study, we propose a new computational method (IILLS) to predict virus-receptor interactions based on Initial Interaction scores method via the neighbors and the Laplacian regularized Least Square algorithm. IILLS integrates the known virus-receptor interactions and amino acid sequences of receptors. The similarity of viruses is calculated by the Gaussian Interaction Profile (GIP) kernel. On the other hand, we also compute the receptor GIP similarity and the receptor sequence similarity. Then the sequence similarity is used as the final similarity of receptors according to the prediction results. The 10-fold cross validation (10CV) and leave one out cross validation (LOOCV) are used to assess the prediction performance of our method. We also compare our method with other three competing methods (BRWH, LapRLS, CMF). Conlusion The experiment results show that IILLS achieves the AUC values of 0.8675 and 0.9061 with the 10-fold cross validation and leave-one-out cross validation (LOOCV), respectively, which illustrates that IILLS is superior to the competing methods. In addition, the case studies also further indicate that the IILLS method is effective for the virus-receptor interaction prediction.

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.

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.

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.

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