scholarly journals MCCMF: Collaborative matrix factorization based on matrix completion for predicting miRNA-disease associations

2020 ◽  
Author(s):  
Tian-Ru Wu ◽  
Meng-Meng Yin ◽  
Cui-Na Jiao ◽  
Ying-Lian Gao ◽  
Xiang-Zhen Kong ◽  
...  
Keyword(s):  
Matrix Factorization ◽  
Cross Validation ◽  
Matrix Completion ◽  
Similarity Matrix ◽  
Validation Experiment ◽  
Disease Associations ◽  
Auc Value ◽  
Regulatory Functions ◽  
Association Matrix ◽  
Fold Cross Validation

Abstract Background: microRNAs (miRNAs) are non-coding RNAs with regulatory functions. Many studies have shown that miRNAs are closely associated with human diseases. Among the methods to explore the relationship between the miRNA and the disease, traditional methods are time-consuming and the accuracy needs to be improved. In view of the shortcoming of previous models, a method, collaborative matrix factorization based on matrix completion (MCCMF) is proposed to predict the unknown miRNA-disease associations.Results: The complete matrix of the miRNA and the disease is obtained by matrix completion. Moreover, Gaussian Interaction Profile (GIP) kernel is added to the miRNA functional similarity matrix and the disease semantic similarity matrix. Then the Weight K Nearest Known Neighbors (WKNKN) method is used to pretreat the association matrix, so the model is close to the reality. Finally, collaborative matrix factorization (CMF) method is applied to obtain the prediction results. Therefore, the MCCMF obtains a satisfactory result in the five-fold cross-validation, with an AUC of 0.9569(0.0005).Conclusions: The AUC value of MCCMF is higher than other advanced methods in the 5-fold cross validation experiment. In order to comprehensively evaluate the performance of MCCMF, accuracy, precision, recall and f-measure are also added. The final experimental results demonstrate that MCCMF outperforms other methods in predicting miRNA-disease associations. In the end, the effectiveness and practicability of MCCMF are further verified by researching three specific diseases.

scholarly journals MCCMF: Collaborative matrix factorization based on matrix completion for predicting miRNA-disease associations

2020 ◽  
Author(s):  
Tian-Ru Wu ◽  
Meng-Meng Yin ◽  
Cui-Na Jiao ◽  
Ying-Lian Gao ◽  
Xiang-Zhen Kong ◽  
...  
Keyword(s):  
Matrix Factorization ◽  
Cross Validation ◽  
Matrix Completion ◽  
Similarity Matrix ◽  
Validation Experiment ◽  
Disease Associations ◽  
Auc Value ◽  
Regulatory Functions ◽  
Association Matrix ◽  
Fold Cross Validation

Abstract Background: microRNAs (miRNAs) are non-coding RNAs with regulatory functions. Many studies have shown that miRNAs are closely associated with human diseases. Among the methods to explore the relationship between the miRNA and the disease, traditional methods are time-consuming and the accuracy needs to be improved. In view of the shortcoming of previous models, a collaborative matrix factorization based on matrix completion (MCCMF) is proposed to predict the unknown miRNA-disease associations.Results: The complete matrix of the miRNA and the disease is obtained by matrix completion. Moreover, Gaussian Interaction Profile (GIP) kernel is added to the miRNA functional similarity matrix and the disease semantic similarity matrix to form the GIP kernel similarity matrix. Then the Weight K Nearest Known Neighbors (WKNKN) method is used to pretreat the association matrix, so the model is close to the reality. Finally, collaborative matrix factorization (CMF) method is applied to obtain the prediction results. Therefore, the MCCMF obtains a satisfactory result in the five-fold cross-validation, with an AUC of 0.9569(0.0005).Conclusions: The AUC value of MCCMF is higher than other advanced methods in the 5-fold cross validation experiment. In order to comprehensively evaluate the performance of MCCMF, accuracy, precision, recall and f-measure are also added. The final experimental results demonstrate that MCCMF outperforms other methods in predicting miRNA-disease associations. In the end, the effectiveness and practicability of MCCMF are further verified by researching three specific diseases.

scholarly journals MCCMF: collaborative matrix factorization based on matrix completion for predicting miRNA-disease associations

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Tian-Ru Wu ◽  
Meng-Meng Yin ◽  
Cui-Na Jiao ◽  
Ying-Lian Gao ◽  
Xiang-Zhen Kong ◽  
...  
Keyword(s):  
Matrix Factorization ◽  
Cross Validation ◽  
Matrix Completion ◽  
Factorization Method ◽  
Similarity Matrix ◽  
Validation Experiment ◽  
Disease Associations ◽  
Auc Value ◽  
Regulatory Functions ◽  
Association Matrix

Abstract Background MicroRNAs (miRNAs) are non-coding RNAs with regulatory functions. Many studies have shown that miRNAs are closely associated with human diseases. Among the methods to explore the relationship between the miRNA and the disease, traditional methods are time-consuming and the accuracy needs to be improved. In view of the shortcoming of previous models, a method, collaborative matrix factorization based on matrix completion (MCCMF) is proposed to predict the unknown miRNA-disease associations. Results The complete matrix of the miRNA and the disease is obtained by matrix completion. Moreover, Gaussian Interaction Profile kernel is added to the miRNA functional similarity matrix and the disease semantic similarity matrix. Then the Weight K Nearest Known Neighbors method is used to pretreat the association matrix, so the model is close to the reality. Finally, collaborative matrix factorization method is applied to obtain the prediction results. Therefore, the MCCMF obtains a satisfactory result in the fivefold cross-validation, with an AUC of 0.9569 (0.0005). Conclusions The AUC value of MCCMF is higher than other advanced methods in the fivefold cross validation experiment. In order to comprehensively evaluate the performance of MCCMF, accuracy, precision, recall and f-measure are also added. The final experimental results demonstrate that MCCMF outperforms other methods in predicting miRNA-disease associations. In the end, the effectiveness and practicability of MCCMF are further verified by researching three specific diseases.

scholarly journals MCCMF: Collaborative matrix factorization based on matrix completion for predicting miRNA-disease associations

2020 ◽  
Author(s):  
Tian-Ru Wu ◽  
Meng-Meng Yin ◽  
Cui-Na Jiao ◽  
Ying-Lian Gao ◽  
Xiang-Zhen Kong ◽  
...  
Keyword(s):  
Matrix Factorization ◽  
Cross Validation ◽  
Matrix Completion ◽  
Similarity Matrix ◽  
Evaluation Indexes ◽  
Validation Experiment ◽  
Disease Associations ◽  
Regulatory Functions ◽  
Association Matrix ◽  
Fold Cross Validation

Abstract Background: MicroRNAs (MiRNAs) are non-coding RNAs with regulatory functions. Many studies have shown that miRNAs are closely associated with human diseases. Among the methods to explore the relationship between the miRNA and the disease, traditional methods are time-consuming and the accuracy needs to be improved. In view of the shortcoming of previous models, a collaborative matrix factorization based on matrix completion (MCCMF) is proposed to predict the unknown miRNA-disease associations.Results: The complete matrix of the miRNA and the disease is obtained by matrix completion. Moreover, Gaussian Interaction Profile (GIP) kernel is added to the miRNA functional similarity matrix and the disease semantic similarity matrix to form the GIP kernel similarity matrix. Then the Weight K Nearest Known Neighbors (WKNKN) method is used to pretreat the association matrix, so the model is close to the reality. Finally, collaborative matrix factorization (CMF) method is applied to obtain the prediction results. Therefore, the MCCMF obtains a satisfactory result in the five-fold cross-validation, with an AUC of 0.9569(0.0005). Conclusions: The AUC value of MCCMF is higher than other advanced methods in the 5-fold cross validation experiment. In order to comprehensively evaluate the performance of MCCMF, f-measure and other evaluation indexes are also added. The final experimental results demonstrate that MCCMF outperforms other methods in prediction miRNA-disease associations. In the end, the effectiveness and practicability of MCCMF are further verified by researching three specific diseases.

scholarly journals MCCMF: Collaborative matrix factorization based on matrix completion for predicting miRNA-disease associations

2020 ◽  
Author(s):  
Tian-Ru Wu ◽  
Meng-Meng Yin ◽  
Cui-Na Jiao ◽  
Jin-Xing Liu ◽  
Ying-Lian Gao ◽  
...  
Keyword(s):  
Matrix Factorization ◽  
Cross Validation ◽  
Matrix Completion ◽  
Similarity Matrix ◽  
Evaluation Indexes ◽  
Validation Experiment ◽  
Disease Associations ◽  
Regulatory Functions ◽  
Association Matrix ◽  
Fold Cross Validation

Abstract Background: MicroRNAs (MiRNAs) are non-coding RNAs with regulatory functions. Many studies have shown that miRNAs are closely associated with human diseases. Among the methods to explore the relationship between the miRNA and the disease, traditional methods are time-consuming and the accuracy needs to be improved. In view of the shortcoming of previous models, a collaborative matrix factorization based on matrix completion (MCCMF) is proposed to predict the unknown miRNA-disease associations. Results: The complete matrix of the miRNA and the disease is obtained by matrix completion. Moreover, Gaussian Interaction Profile (GIP) kernel is added to the miRNA functional similarity matrix and the disease semantic similarity matrix to form the GIP kernel similarity matrix. Then the Weight K Nearest Known Neighbors (WKNKN) method is used to pretreat the association matrix, so the model is close to the reality. Finally, collaborative matrix factorization (CMF) method is applied to obtain the prediction results. Therefore, the MCCMF obtains a satisfactory result in the five-fold cross-validation, with an AUC of 0.9569(0.0005). Conclusions: The AUC value of MCCMF is higher than other advanced methods in the 5-fold cross validation experiment. In order to comprehensively evaluate the performance of MCCMF, f-measure and other evaluation indexes are also added. The final experimental results demonstrate that MCCMF outperforms other methods in prediction miRNA-disease associations. In the end, the effectiveness and practicability of MCCMF are further verified by researching three specific diseases.

scholarly journals Double matrix completion for circRNA-disease association prediction

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Zong-Lan Zuo ◽  
Rui-Fen Cao ◽  
Pi-Jing Wei ◽  
Jun-Feng Xia ◽  
Chun-Hou Zheng
Keyword(s):  
Cross Validation ◽  
Matrix Completion ◽  
Matrix Multiplication ◽  
Computational Method ◽  
Sequence Information ◽  
Potential Candidate ◽  
Similarity Matrix ◽  
Matrix Block ◽  
Disease Associations ◽  
Association Matrix

Abstract Background Circular RNAs (circRNAs) are a class of single-stranded RNA molecules with a closed-loop structure. A growing body of research has shown that circRNAs are closely related to the development of diseases. Because biological experiments to verify circRNA-disease associations are time-consuming and wasteful of resources, it is necessary to propose a reliable computational method to predict the potential candidate circRNA-disease associations for biological experiments to make them more efficient. Results In this paper, we propose a double matrix completion method (DMCCDA) for predicting potential circRNA-disease associations. First, we constructed a similarity matrix of circRNA and disease according to circRNA sequence information and semantic disease information. We also built a Gauss interaction profile similarity matrix for circRNA and disease based on experimentally verified circRNA-disease associations. Then, the corresponding circRNA sequence similarity and semantic similarity of disease are used to update the association matrix from the perspective of circRNA and disease, respectively, by matrix multiplication. Finally, from the perspective of circRNA and disease, matrix completion is used to update the matrix block, which is formed by splicing the association matrix obtained in the previous step with the corresponding Gaussian similarity matrix. Compared with other approaches, the model of DMCCDA has a relatively good result in leave-one-out cross-validation and five-fold cross-validation. Additionally, the results of the case studies illustrate the effectiveness of the DMCCDA model. Conclusion The results show that our method works well for recommending the potential circRNAs for a disease for biological experiments.

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

Genes ◽  
2019 ◽  
Vol 10 (8) ◽  
pp. 608 ◽  
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.

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 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.

scholarly journals DSCMF: prediction of LncRNA-disease associations based on dual sparse collaborative matrix factorization

2021 ◽  
Vol 22 (S3) ◽  
Author(s):  
Jin-Xing Liu ◽  
Ming-Ming Gao ◽  
Zhen Cui ◽  
Ying-Lian Gao ◽  
Feng Li
Keyword(s):  
Matrix Factorization ◽  
Factorization Method ◽  
Treatment And Prevention ◽  
Cancer Breast ◽  
Disease Associations ◽  
Network Similarity ◽  
Huge Impact ◽  
Auc Value ◽  
Fold Cross Validation

Abstract Background In the development of science and technology, there are increasing evidences that there are some associations between lncRNAs and human diseases. Therefore, finding these associations between them will have a huge impact on our treatment and prevention of some diseases. However, the process of finding the associations between them is very difficult and requires a lot of time and effort. Therefore, it is particularly important to find some good methods for predicting lncRNA-disease associations (LDAs). Results In this paper, we propose a method based on dual sparse collaborative matrix factorization (DSCMF) to predict LDAs. The DSCMF method is improved on the traditional collaborative matrix factorization method. To increase the sparsity, the L2,1-norm is added in our method. At the same time, Gaussian interaction profile kernel is added to our method, which increase the network similarity between lncRNA and disease. Finally, the AUC value obtained by the experiment is used to evaluate the quality of our method, and the AUC value is obtained by the ten-fold cross-validation method. Conclusions The AUC value obtained by the DSCMF method is 0.8523. At the end of the paper, simulation experiment is carried out, and the experimental results of prostate cancer, breast cancer, ovarian cancer and colorectal cancer are analyzed in detail. The DSCMF method is expected to bring some help to lncRNA-disease associations research. The code can access the https://github.com/Ming-0113/DSCMF website.

scholarly journals RCMF: a robust collaborative matrix factorization method to predict miRNA-disease associations

2019 ◽  
Vol 20 (S25) ◽  
Author(s):  
Zhen Cui ◽  
Jin-Xing Liu ◽  
Ying-Lian Gao ◽  
Chun-Hou Zheng ◽  
Juan Wang
Keyword(s):  
Matrix Factorization ◽  
Factorization Method ◽  
Simulation Experiments ◽  
Gold Standard Dataset ◽  
Disease Associations ◽  
Auc Value ◽  
Accuracy Of Prediction ◽  
Fold Cross Validation ◽  
Novel Associations ◽  
Better Than

Abstract Background Predicting miRNA-disease associations (MDAs) is time-consuming and expensive. It is imminent to improve the accuracy of prediction results. So it is crucial to develop a novel computing technology to predict new MDAs. Although some existing methods can effectively predict novel MDAs, there are still some shortcomings. Especially when the disease matrix is processed, its sparsity is an important factor affecting the final results. Results A robust collaborative matrix factorization (RCMF) is proposed to predict novel MDAs. The L2,1-norm are introduced to our method to achieve the highest AUC value than other advanced methods. Conclusions 5-fold cross validation is used to evaluate our method, and simulation experiments are used to predict novel associations on Gold Standard Dataset. Finally, our prediction accuracy is better than other existing advanced methods. Therefore, our approach is effective and feasible in predicting novel MDAs.

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