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collaborative matrix factorization with Soft Regularization for Drug-Target Interaction Prediction

Journal of Computer Science & Technology 2021年第2期36卷 310-322页

作者： Li-Gang Gao Meng-Yun Yang Jian-Xin Wang School of Computer Science and Engineering Central South UniversityChangsha 410083China Hunan Provincial Key Laboratory of Bioinformatics Central South UniversityChangsha 410083China School of Science Shaoyang UniversityShaoyang 422000China

Identifying the potential drug-target interactions(DTI)is critical in drug *** drug-target interaction prediction methods based on collaborative filtering have demonstrated attractive prediction ***,many corresponding models cannot accurately express the relationship between similarity features and DTI *** order to rationally represent the correlation,we propose a novel matrix factorization method,so-called collaborative matrix factorization with soft regularization(SRCMF).SRCMF improves the prediction performance by combining the drug and the target similarity information with matrix *** contrast to general collaborative matrix factorization,the fundamental idea of SRCMF is to make the similarity features and the potential features of DTI approximate,not ***,SRCMF obtains low-rank feature representations of drug similarity and target similarity,and then uses a soft regularization term to constrain the approximation between drug(target)similarity features and drug(target)potential features of *** comprehensively evaluate the prediction performance of SRCMF,we conduct cross-validation experiments under three different *** terms of the area under the precision-recall curve(AUPR),SRCMF achieves better prediction results than six state-of-the-art ***,under different noise levels of similarity data,the prediction performance of SRCMF is much better than that of collaborative matrix *** conclusion,SRCMF is robust leading to performance improvement in drug-target interaction prediction.

关键词： drug-target interaction collaborative matrix factorization soft regularization noisy data

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collaborative matrix factorization mechanism for group recommendation in big data-based library systems

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LIBRARY HI TECH 2018年第3期36卷 458-481页

作者： Liu, Yezheng Yang, Lu Sun, Jianshan Jiang, Yuanchun Wang, Jinkun Hefei Univ Technol Sch Management Hefei Anhui Peoples R China

Purpose Academic groups are designed specifically for researchers. A group recommendation procedure is essential to support scholars' research-based social activities. However, group recommendation methods are rarely applied in online libraries and they often suffer from scalability problem in big data context. The purpose of this paper is to facilitate academic group activities in big data-based library systems by recommending satisfying articles for academic groups. Design/methodology/approach The authors propose a collaborative matrix factorization (CoMF) mechanism and implement paralleled CoMF under Hadoop framework. Its rationale is collaboratively decomposing researcher-article interaction matrix and group-article interaction matrix. Furthermore, three extended models of CoMF are proposed. Findings Empirical studies on CiteULike data set demonstrate that CoMF and three variants outperform baseline algorithms in terms of accuracy and robustness. The scalability evaluation of paralleled CoMF shows its potential value in scholarly big data environment. Research limitations/implications The proposed methods fill the gap of group-article recommendation in online libraries domain. The proposed methods have enriched the group recommendation methods by considering the interaction effects between groups and members. The proposed methods are the first attempt to implement group recommendation methods in big data contexts. Practical implications The proposed methods can improve group activity effectiveness and information shareability in academic groups, which are beneficial to membership retention and enhance the service quality of online library systems. Furthermore, the proposed methods are applicable to big data contexts and make library system services more efficient. Social implications The proposed methods have potential value to improve scientific collaboration and research innovation. Originality/value The proposed CoMF method is a novel group recommendation met

关键词： Big data analytics collaborative matrix factorization Group recommendation Online library system Personalized services Scientific article recommendation

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LWPCMF: Logistic Weighted Profile-Based collaborative matrix factorization for Predicting MiRNA-Disease Associations

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021年第3期18卷 1122-1129页

作者： Yin, Meng-Meng Cui, Zhen Gao, Ming-Ming Liu, Jin-Xing Gao, Ying-Lian Qufu Normal Univ Sch Informat Sci & Engn Rizhao 276826 Shandong Peoples R China Qufu Normal Univ Lib Rizhao 276826 Shandong Peoples R China

As is known to all, constructing experiments to predict unknown miRNA-disease association is time-consuming, laborious and costly. Accordingly, new prediction model should be conducted to predict novel miRNA-disease associations. What's more, the performance of this method should be high and reliable. In this paper, a new computation model Logistic Weighted Profile-based collaborative matrix factorization (LWPCMF) is put forward. In this method, weighted profile (WP) is combined with collaborative matrix factorization (CMF) to increase the performance of this model. And, the neighbor information is considered. In addition, logistic function is applied to miRNA functional similarity matrix and disease semantic similarity matrix to extract valuable information. At the same time, by adding WP and logistic function, the known correlation can be protected. And, Gaussian Interaction Profile (GIP) kernels of miRNAs and diseases are added to miRNA functional similarity network and disease semantic similarity network to augment kernel similarities. Then, a five-fold cross validation is implemented to evaluate the predictive ability of this method. Besides, case studies are conducted to view the experimental results. The final result contains not only known associations but also newly predicted ones. And, the result proves that our method is better than other existing methods. This model is able to predict potential miRNA-disease associations.

关键词： Diseases Kernel Logistics Semantics Computational modeling Databases Collaboration miRNA-disease association collaborative matrix factorization logistic function weighted profile

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WGRCMF: A Weighted Graph Regularized collaborative matrix factorization Method for Predicting Novel LncRNA-Disease Associations

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2021年第1期25卷 257-265页

作者： Liu, Jin-Xing Cui, Zhen Gao, Ying-Lian Kong, Xiang-Zhen Qufu Normal Univ Sch Informat Sci & Engn Rizhao 276826 Peoples R China Qufu Normal Univ Qufu Normal Univ Lib Rizhao 276826 Peoples R China

In recent years, many human diseases have been determined to be associated with certain lncRNAs. Only a small percentage of all lncRNA-disease associations (LDAs) have been discovered by researchers. Predicting novel LDAs is time-consuming and costly. It is crucial to propose a method that can effectively identify potential LDAs to solve this problem based on the available datasets. Although some current methods can effectively predict potential LDAs, the prediction accuracy needs to be improved, and there are few known associations. Moreover, there are notable errors in the method of constructing the network and the bipartite graph, which interfere with the final results. A weighted graph regularized collaborative matrix factorization (WGRCMF) method is proposed to predict novel LDAs. We introduce the graph regularization terms into the collaborative matrix factorization. Considering that manifold learning can recover low-dimensional manifold structures from high-dimensional sampled data, we can find low-dimensional manifolds in high-dimensional space. In addition, a weight matrix is also introduced into the method, the significance of which is to prevent unknown associations from contributing to the final prediction matrix. Finally, the prediction accuracy of this method is better than those of other methods. In several cancer cases, we implemented the corresponding simulation experiments. According to the experimental results, the proposed method is feasible and effective.

关键词： Cancer Semantics Collaboration Kernel Databases matrix decomposition LncRNA-disease associations graph regularization gaussian kernel collaborative matrix factorization

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LDCMFC: Predicting Long Non-Coding RNA and Disease Association Using collaborative matrix factorization Based on Correntropy

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023年第3期20卷 1774-1782页

作者： Xi, Wen-Yu Zhou, Feng Gao, Ying-Lian Liu, Jin-Xing Zheng, Chun-Hou Qufu Normal Univ Sch Comp Sci Rizhao 276826 Shandong Peoples R China Qufu Normal Univ Qufu Normal Univ Lib Rizhao 276826 Shandong Peoples R China

With the development of bioinformatics, the important role played by lncRNAs in various intractable diseases has aroused the interest of many experts. In recent studies, researchers have found that several human diseases are related to lncRANs. Moreover, it is very difficult and expensive to explore the unknown lncRNA-disease associations (LDAs), so only a few associations have been confirmed. It is vital to find a more accurate and effective method to identify potential LDAs. In this study, a method of collaborative matrix factorization based on correntropy (LDCMFC) is proposed for the identification of potential LDAs. To improve the robustness of the algorithm, the traditional minimization of the Euclidean distance is replaced with the maximized correntropy. In addition, the weighted K nearest known neighbor (WKNKN) method is used to rebuild the adjacency matrix. Finally, the performance of LDCMFC is tested by 5-fold cross-validation. Compared with other traditional methods, LDACMFC obtains a higher AUC of 0.8628. In different types of studies of three important cancer cases, most of the potentially relevant lncRNAs derived from the experiments have been validated in the databases. The final result shows that LDCMFC is a feasible method to predict LDAs.

关键词： LncRNA-disease association prediction correntropy collaborative matrix factorization

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Multi-Label Fusion collaborative matrix factorization for Predicting LncRNA-Disease Associations

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2021年第3期25卷 881-890页

作者： Gao, Ming-Ming Cui, Zhen Gao, Ying-Lian Wang, Juan Liu, Jin-Xing Qufu Normal Univ Sch Informat Sci & Engn Rizhao 276826 Shandong Peoples R China Qufu Normal Univ Qufu Normal Univ Lib Rizhao 276826 Shandong Peoples R China

As we all know, science and technology are developing faster and faster. Many experts and scholars have demonstrated that human diseases are related to lncRNA, but only a few associations have been confirmed, and many unknown associations need to be found. In the process of finding associations, it takes a lot of time, so finding an efficient way to predict the associations between lncRNAs and diseases is particularly important. In this paper, we propose a multi-label fusion collaborative matrix factorization (MLFCMF) approach for predicting lncRNA-disease associations (LDAs). Firstly, the lncRNA space and disease space are optimized by multi-label to enhance the intrinsic link between lncRNA and disease and to tap potential information. Multi-label learning can encode a variety of data information from the sample space. Secondly, to learn multi-label information in the data space, the fusion method is used to handle the relationship between multiple labels. More comprehensive information will be obtained by weighing the effects of different labels. The addition of Gaussian interaction profile (GIP) kernel can increase the network similarity. Finally, the lncRNA-disease associations are predicted by the method of collaborative matrix factorization. The ten-fold cross-validation method is used to evaluate the MLFCMF method, and our method finally obtains an AUC value of 0.8612. Detailed analysis of ovarian cancer, colorectal cancer, and lung cancer in the simulation experiment results. So it can be seen that our method MLFCMF is an effective model for predicting lncRNA-disease associations.

关键词： Cancer Semantics Collaboration Directed acyclic graph Neoplasms Information science collaborative matrix factorization Gaussian interaction profile LncRNA-disease associations simulation experiment

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CMFHMDA: collaborative matrix factorization for Human Microbe-Disease Association Prediction 1

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13th International Conference on Intelligent Computing (ICIC)

作者： Shen, Zhen Jiang, Zhichao Bao, Wenzheng Tongji Univ Inst Machine Learning & Syst Biol Sch Elect & Informat Engn Shanghai 201804 Peoples R China

ISBN: (数字)9783319633121

ISBN: (纸本)9783319633121;9783319633114

The research on microorganisms indicates that microbes are abundant in human body, which have closely connection with various human noninfectious diseases. The deep research of microbe-disease associations is not only helpful to timely diagnosis and treatment of human diseases, but also facilitates the development of new drugs. However, the current knowledge in this domain is still limited and far from complete. Here, we proposed the computational model of collaborative matrix factorization for Human Microbe-Disease Association prediction (CMFHMDA) by integrating known microbe-disease associations and Gaussian interaction profile kernel similarity for microbes and diseases. A special matrix factorization algorithm was introduced here to update the correlation matrix about microbes and diseases for inferring the most possible diseaserelated microbes. Leave-one-out Cross Validation (LOOCV) and k-fold cross Validation were implemented to evaluate the prediction performance of this model. As a result, CMFHMDA obtained AUCs of 0.8858 and 0.8529 based on 5-fold cross validation and Global LOOCV, respectively. It is no doubt that CMFHMDA could be used to identify more potential microbes associated with important noninfectious human diseases.

关键词： Microbe Disease Similarity collaborative matrix factorization Gaussian interaction profile

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

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BMC BIOINFORMATICS 2021年第SUPPL 3期22卷 241-241页

作者： Liu, Jin-Xing Gao, Ming-Ming Cui, Zhen Gao, Ying-Lian Li, Feng Qufu Normal Univ Sch Comp Sci Rizhao Peoples R China Qufu Normal Univ Qufu Normal Univ Lib Rizhao Peoples R China

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 L-2,L-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://***/Ming-.0113/DSCMF website.

关键词： LncRNA-disease associations Gaussian interaction profile kernel collaborative matrix factorization

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