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.

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 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 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 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 ◽  
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 ◽  
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 NPCMF: Nearest Profile-based Collaborative Matrix Factorization method for predicting miRNA-disease associations

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Ying-Lian Gao ◽  
Zhen Cui ◽  
Jin-Xing Liu ◽  
Juan Wang ◽  
Chun-Hou Zheng
Keyword(s):  
Matrix Factorization ◽  
Factorization Method ◽  
Disease Associations

scholarly journals NEDD: a network embedding based method for predicting drug-disease associations

2020 ◽  
Vol 21 (S13) ◽  
Author(s):  
Renyi Zhou ◽  
Zhangli Lu ◽  
Huimin Luo ◽  
Ju Xiang ◽  
Min Zeng ◽  
...  
Keyword(s):  
Drug Repositioning ◽  
Computational Method ◽  
Heterogeneous Information ◽  
Gold Standard Dataset ◽  
Disease Similarity ◽  
Disease Associations ◽  
Meta Path ◽  
Approved Drugs ◽  
Low Dimensional ◽  
Novel Associations

Abstract Background Drug discovery is known for the large amount of money and time it consumes and the high risk it takes. Drug repositioning has, therefore, become a popular approach to save time and cost by finding novel indications for approved drugs. In order to distinguish these novel indications accurately in a great many of latent associations between drugs and diseases, it is necessary to exploit abundant heterogeneous information about drugs and diseases. Results In this article, we propose a meta-path-based computational method called NEDD to predict novel associations between drugs and diseases using heterogeneous information. First, we construct a heterogeneous network as an undirected graph by integrating drug-drug similarity, disease-disease similarity, and known drug-disease associations. NEDD uses meta paths of different lengths to explicitly capture the indirect relationships, or high order proximity, within drugs and diseases, by which the low dimensional representation vectors of drugs and diseases are obtained. NEDD then uses a random forest classifier to predict novel associations between drugs and diseases. Conclusions The experiments on a gold standard dataset which contains 1933 validated drug–disease associations show that NEDD produces superior prediction results compared with the state-of-the-art approaches.

scholarly journals A Probabilistic Matrix Factorization Method for Identifying lncRNA-disease Associations

Genes ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 126 ◽  
Author(s):  
Zhanwei Xuan ◽  
Jiechen Li ◽  
Jingwen Yu ◽  
Xiang Feng ◽  
Bihai Zhao ◽  
...  
Keyword(s):  
Computational Models ◽  
Matrix Decomposition ◽  
Factorization Method ◽  
Association Network ◽  
Disease Associations ◽  
Probabilistic Matrix Factorization ◽  
Simulation Results ◽  
Artery Disease ◽  
Leave One Out ◽  
Better Than

Recently, an increasing number of studies have indicated that long-non-coding RNAs (lncRNAs) can participate in various crucial biological processes and can also be used as the most promising biomarkers for the treatment of certain diseases such as coronary artery disease and various cancers. Due to costs and time complexity, the number of possible disease-related lncRNAs that can be verified by traditional biological experiments is very limited. Therefore, in recent years, it has been very popular to use computational models to predict potential disease-lncRNA associations. In this study, we constructed three kinds of association networks, namely the lncRNA-miRNA association network, the miRNA-disease association network, and the lncRNA-disease correlation network firstly. Then, through integrating these three newly constructed association networks, we constructed an lncRNA-disease weighted association network, which would be further updated by adopting the KNN algorithm based on the semantic similarity of diseases and the similarity of lncRNA functions. Thereafter, according to the updated lncRNA-disease weighted association network, a novel computational model called PMFILDA was proposed to infer potential lncRNA-disease associations based on the probability matrix decomposition. Finally, to evaluate the superiority of the new prediction model PMFILDA, we performed Leave One Out Cross-Validation (LOOCV) based on strongly validated data filtered from MNDR and the simulation results indicated that the performance of PMFILDA was better than some state-of-the-art methods. Moreover, case studies of breast cancer, lung cancer, and colorectal cancer were implemented to further estimate the performance of PMFILDA, and simulation results illustrated that PMFILDA could achieve satisfying prediction performance as well.

scholarly journals GATCDA: Predicting circRNA-Disease Associations Based on Graph Attention Network

Cancers ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2595
Author(s):  
Chen Bian ◽  
Xiu-Juan Lei ◽  
Fang-Xiang Wu
Keyword(s):  
Computational Method ◽  
Circular Rnas ◽  
Attention Network ◽  
Rna Molecules ◽  
Non Coding Rna ◽  
Circular Structure ◽  
Disease Associations ◽  
Network Similarity ◽  
Auc Value ◽  
Fold Cross Validation

CircRNAs (circular RNAs) are a class of non-coding RNA molecules with a closed circular structure. CircRNAs are closely related to the occurrence and development of diseases. Due to the time-consuming nature of biological experiments, computational methods have become a better way to predict the interactions between circRNAs and diseases. In this study, we developed a novel computational method called GATCDA utilizing a graph attention network (GAT) to predict circRNA–disease associations with disease symptom similarity, network similarity, and information entropy similarity for both circRNAs and diseases. GAT learns representations for nodes on a graph by an attention mechanism, which assigns different weights to different nodes in a neighborhood. Considering that the circRNA–miRNA–mRNA axis plays an important role in the generation and development of diseases, circRNA–miRNA interactions and disease–mRNA interactions were adopted to construct features, in which mRNAs were related to 88% of miRNAs. As demonstrated by five-fold cross-validation, GATCDA yielded an AUC value of 0.9011. In addition, case studies showed that GATCDA can predict unknown circRNA–disease associations. In conclusion, GATCDA is a useful method for exploring associations between circRNAs and diseases.

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