Collaborative Matrix Factorization Research Articles

Heterogeneous graph inference with range constrainted [formula omitted]-collaborative matrix factorization for small molecule-miRNA association prediction

MicroRNAs (miRNAs) play a vital role in regulating gene expression and various biological processes. As a result, they have been identified as effective targets for small molecule (SM) drugs in disease treatment. Heterogeneous graph inference stands as a classical approach for predicting SM-miRNA associations, showcasing commendable convergence accuracy and speed. However, most existing methods do not adequately address the inherent sparsity in SM-miRNA association networks, and imprecise SM/miRNA similarity metrics reduce the accuracy of predicting SM-miRNA associations. In this research, we proposed a heterogeneous graph inference with range constrained L2,1-collaborative matrix factorization (HGIRCLMF) method to predict potential SM-miRNA associations. First, we computed the multi-source similarities of SM/miRNA and integrated these similarity information into a comprehensive SM/miRNA similarity. This step improved the accuracy of SM and miRNA similarity, ensuring reliability for the subsequent inference of the heterogeneity map. Second, we used a range constrained L2,1-collaborative matrix factorization (RCLMF) model to pre-populate the SM-miRNA association matrix with missing values. In this step, we developed a novel matrix decomposition method that enhances the robustness and formative nature of SM-miRNA edges between SM networks and miRNA networks. Next, we built a well-established SM-miRNA heterogeneous network utilizing the processed biological information. Finally, HGIRCLMF used this network data to infer unknown association pair scores. We implemented four cross-validation experiments on two distinct datasets, and HGIRCLMF acquired the highest areas under the curve, surpassing six state-of-the-art computational approaches. Furthermore, we performed three case studies to validate the predictive power of our method in practical application.

AsCDPR: a novel framework for ratings and personalized preference hotel recommendation using cross-domain and aspect-based features

PurposeThe explosion of data due to the sophistication of information and communication technology makes it simple for prospective tourists to learn about previous hotel guests' experiences. They prioritize the rating score when selecting a hotel. However, rating scores are less reliable for suggesting a personalized preference for each aspect, especially when they are in a limited number. This study aims to recommend ratings and personalized preference hotels using cross-domain and aspect-based features.Design/methodology/approachWe propose an aspect-based cross-domain personalized recommendation (AsCDPR), a novel framework for rating prediction and personalized customer preference recommendations. We incorporate a cross-domain personalized approach and aspect-based features of items from the review text. We extracted aspect-based feature vectors from two domains using bidirectional long short-term memory and then mapped them by a multilayer perceptron (MLP). The cross-domain recommendation module trains MLP to analyze sentiment and predict item ratings and the polarities of the aspect based on user preferences.FindingsExpanded by its synonyms, aspect-based features significantly improve the performance of sentiment analysis on accuracy and the F1-score matrix. With relatively low mean absolute error and root mean square error values, AsCDPR outperforms matrix factorization, collaborative matrix factorization, EMCDPR and Personalized transfer of user preferences for cross-domain recommendation. These values are 1.3657 and 1.6682, respectively.Research limitation/implicationsThis study assists users in recommending hotels based on their priority preferences. Users do not need to read other people's reviews to capture the key aspects of items. This model could enhance system reliability in the hospitality industry by providing personalized recommendations.Originality/valueThis study introduces a new approach that embeds aspect-based features of items in a cross-domain personalized recommendation. AsCDPR predicts ratings and provides recommendations based on priority aspects of each user's preferences.

LDCMFC: Predicting Long Non-Coding RNA and Disease Association Using Collaborative Matrix Factorization Based on Correntropy.

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.

How to optimize Recommendation System Performance using Deep Neural Network based Graph Architecture

In this research, would bring a description and comparison of how the Deep learning-based Graph neural Network actually outperforms the other similar recommender system like collaborative filtering, content-based filtering, SVD, Matrix Factorization and few others. This is basically achieved by exposing the correct relation between the objects through a graph architecture and the dependency and inter correlation between them. Would also like to share an in-depth analysis and understanding of how the Graph architecture works and the underlying theories. This could be either a TensorFlow based architecture or a Pytorch based architecture but in this paper will mainly focus on the TensorFlow one for its flexibility and cloud friendly nature for adopting in any framework.
 

Identification of drug-target interactions via multiple kernel-based triple collaborative matrix factorization.

Targeted drugs have been applied to the treatment of cancer on a large scale, and some patients have certain therapeutic effects. It is a time-consuming task to detect drug-target interactions (DTIs) through biochemical experiments. At present, machine learning (ML) has been widely applied in large-scale drug screening. However, there are few methods for multiple information fusion. We propose a multiple kernel-based triple collaborative matrix factorization (MK-TCMF) method to predict DTIs. The multiple kernel matrices (contain chemical, biological and clinical information) are integrated via multi-kernel learning (MKL) algorithm. And the original adjacency matrix of DTIs could be decomposed into three matrices, including the latent feature matrix of the drug space, latent feature matrix of the target space and the bi-projection matrix (used to join the two feature spaces). To obtain better prediction performance, MKL algorithm can regulate the weight of each kernel matrix according to the prediction error. The weights of drug side-effects and target sequence are the highest. Compared with other computational methods, our model has better performance on four test data sets.

Dual-Network Collaborative Matrix Factorization for predicting small molecule-miRNA associations.

MicroRNAs (miRNAs) play crucial roles in multiple biological processes and human diseases and can be considered as therapeutic targets of small molecules (SMs). Because biological experiments used to verify SM-miRNA associations are time-consuming and expensive, it is urgent to propose new computational models to predict new SM-miRNA associations. Here, we proposed a novel method called Dual-network Collaborative Matrix Factorization (DCMF) for predicting the potential SM-miRNA associations. Firstly, we utilized the Weighted K Nearest Known Neighbors (WKNKN) method to preprocess SM-miRNA association matrix. Then, we constructed matrix factorization model to obtain two feature matrices containing latent features of SM and miRNA, respectively. Finally, the predicted SM-miRNA association score matrix was obtained by calculating the inner product of two feature matrices. The main innovations of this method were that the use of WKNKN method can preprocess the missing values of association matrix and the introduction of dual network can integrate more diverse similarity information into DCMF. For evaluating the validity of DCMF, we implemented four different cross validations (CVs) based on two distinct datasets and two different case studies. Finally, based on dataset 1 (dataset 2), DCMF achieved Area Under receiver operating characteristic Curves (AUC) of 0.9868 (0.8770), 0.9833 (0.8836), 0.8377 (0.7591) and 0.9836 ± 0.0030 (0.8632 ± 0.0042) in global Leave-One-Out Cross Validation (LOOCV), miRNA-fixed local LOOCV, SM-fixed local LOOCV and 5-fold CV, respectively. For case studies, plenty of predicted associations have been confirmed by published experimental literature. Therefore, DCMF is an effective tool to predict potential SM-miRNA associations.

Bipartite graph-based collaborative matrix factorization method for predicting miRNA-disease associations

BackgroundWith 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.ResultsBy 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.ConclusionsFive-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.

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

BackgroundIn 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).ResultsIn 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.ConclusionsThe 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.

Identification of Microbe-Drug Association based on Weighted Profile and Collaborative Matrix Factorization

Identification of Microbe-Drug Association based on Weighted Profile and Collaborative Matrix Factorization

Collaborative Matrix Factorization with Soft Regularization for Drug-Target Interaction Prediction

Identifying the potential drug-target interactions (DTI) is critical in drug discovery. The drug-target interaction prediction methods based on collaborative filtering have demonstrated attractive prediction performance. However, many corresponding models cannot accurately express the relationship between similarity features and DTI features. In 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 factorization. In 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 identical. Specifically, 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 DTI. To comprehensively evaluate the prediction performance of SRCMF, we conduct cross-validation experiments under three different settings. In terms of the area under the precision-recall curve (AUPR), SRCMF achieves better prediction results than six state-of-the-art methods. Besides, under different noise levels of similarity data, the prediction performance of SRCMF is much better than that of collaborative matrix factorization. In conclusion, SRCMF is robust leading to performance improvement in drug-target interaction prediction.

Prediction of Virus-Receptor Interactions Based on Improving Similarities.

Viral infectious diseases have been seriously threatening human health. The receptor binding is the first step of viral infection. Predicting virus-receptor interactions will be helpful for the interaction mechanism of viruses and receptors, and further find some effective ways of preventing and treating viral infectious diseases so as to reduce the morbidity and mortality caused by viruses. Some computation algorithms have been proposed for identifying potential virus-receptor interactions. However, a common problem in those methods is the presence of noise in the similarity network. A new computational model (Network Enhancement and the Regularized Least Squares [NERLS]) is proposed to predict virus-receptor interactions based on improving similarities by Network Enhancement (NE). NERLS integrates the virus sequence similarity, the receptor sequence similarity and known virus-receptor interactions. We compute the virus sequence similarity and known virus-receptor interactions to construct the virus similarity network. The receptor similarity network is constructed by the Gaussian interaction profile kernel similarity and the receptor sequence similarity. To obtain the final virus similarity network and the final receptor similarity network, NE is, respectively, applied for reducing the noise of the virus similarity network and the receptor similarity network. Finally, NERLS employs the regularized least squares to predict interactions of viruses and receptors. The experiment results show that NERLS achieves the area under curve value of 0.893 and 0.921 in 10-fold cross-validation and leave-one-out cross-validation, respectively, which is consistently superior to four related methods [which include Initial interaction scores method via the neighbors and the Laplacian regularized Least Square (IILLS), Bi-random walk on a heterogeneous network (BRWH), Laplacian regularized least squares classifier (LapRLS), and Collaborative matrix factorization (CMF)]. Furthermore, a case study also demonstrates that NERLS effectively predicts potential virus-receptor interactions.

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

BackgroundMicroRNAs (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.ResultsThe 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).ConclusionsThe 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.

Multi-Label Fusion Collaborative Matrix Factorization for Predicting LncRNA-Disease Associations

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.

WGRCMF: A Weighted Graph Regularized Collaborative Matrix Factorization Method for Predicting Novel LncRNA-Disease Associations.

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.

Low-Rank Regularized Deep Collaborative Matrix Factorization for Micro-Video Multi-Label Classification

Deep matrix factorization can be regarded as an extension of traditional matrix factorization to help improve applications like social image tag refinement, image retrieval, and face clustering. Toward this tendency, in this letter, we proposed a low-rank regularized deep collaborative matrix factorization (LRDCMF) method to better tackle micro-video multi-label classification tasks. The proposed method aims to collaboratively learn two sets of factor matrices for characterization of latent attributes and two deep representations for instances and labels, respectively. During factorization process, the inverse covariance constraints are exploited to capture the latent correlation structures among latent attributes and labels and the low-dimensional intrinsic deep representations are ensured by further considering low-rank constraints. Moreover, a triplet term that connects instances representations, label representations, and labels is constructed to increase discrimination power of our method. Experimental results conducted on a large-scale micro-video dataset illustrate our model achieves superior performance in comparison with state-of-the-art methods.

Unsupervised Deep Cross-modal Hashing with Virtual Label Regression

Unsupervised cross-modal hashing learns hash codes without dependence on the semantic labels. It has the desirable advantage of well scalability and thus can effectively support large-scale cross-media retrieval. However, how to directly learn discriminative discrete hash codes under the unsupervised learning paradigm is still an open challenging problem. In this paper, we aim to attack this problem by proposing an Unsupervised Deep Cross-modal Hashing with Virtual Label Regression (UDCH-VLR) method. We propose a novel unified learning framework to jointly perform deep hash function training, virtual label learning and regression. Specifically, we learn unified hash codes via collaborative matrix factorization on the multi-modal deep representations to preserve the multi-modal shared semantics. Further, we incorporate the virtual label learning into the objective functions and simultaneously regress the learned virtual labels to the hash codes. Finally, instead of simply exploiting the existing shallow features and relaxing the binary constraints, we devise an alternative optimization strategy to directly update the deep hash functions and discrete binary codes. Under such circumstance, the discriminative capability of hash codes can be progressively enhanced with iterative learning. Extensive experiments on three publicly available cross-media retrieval datasets demonstrate that our approach outperforms state-of-the-art methods.

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

BackgroundPredicting 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.ResultsA 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.Conclusions5-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.

Differentiating isoform functions with collaborative matrix factorization.

Isoforms are alternatively spliced mRNAs of genes. They can be translated into different functional proteoforms, and thus greatly increase the functional diversity of protein variants (or proteoforms). Differentiating the functions of isoforms (or proteoforms) helps understanding the underlying pathology of various complex diseases at a deeper granularity. Since existing functional genomic databases uniformly record the annotations at the gene-level, and rarely record the annotations at the isoform-level, differentiating isoform functions is more challenging than the traditional gene-level function prediction. Several approaches have been proposed to differentiate the functions of isoforms. They generally follow the multi-instance learning paradigm by viewing each gene as a bag and the spliced isoforms as its instances, and push functions of bags onto instances. These approaches implicitly assume the collected annotations of genes are complete and only integrate multiple RNA-seq datasets. As such, they have compromised performance. We propose a data integrative solution (called DisoFun) to Differentiate isoform Functions with collaborative matrix factorization. DisoFun assumes the functional annotations of genes are aggregated from those of key isoforms. It collaboratively factorizes the isoform data matrix and gene-term data matrix (storing Gene Ontology annotations of genes) into low-rank matrices to simultaneously explore the latent key isoforms, and achieve function prediction by aggregating predictions to their originating genes. In addition, it leverages the PPI network and Gene Ontology structure to further coordinate the matrix factorization. Extensive experimental results show that DisoFun improves the area under the receiver operating characteristic curve and area under the precision-recall curve of existing solutions by at least 7.7 and 28.9%, respectively. We further investigate DisoFun on four exemplar genes (LMNA, ADAM15, BCL2L1 and CFLAR) with known functions at the isoform-level, and observed that DisoFun can differentiate functions of their isoforms with 90.5% accuracy. The code of DisoFun is available at mlda.swu.edu.cn/codes.php?name=DisoFun. Supplementary data are available at Bioinformatics online.

LWPCMF: Logistic Weighted Profile-Based Collaborative Matrix Factorization for Predicting MiRNA-Disease Associations.

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.

Multi-View Multi-Instance Multi-Label Learning Based on Collaborative Matrix Factorization

Multi-view Multi-instance Multi-label Learning (M3L) deals with complex objects encompassing diverse instances, represented with different feature views, and annotated with multiple labels. Existing M3L solutions only partially explore the inter or intra relations between objects (or bags), instances, and labels, which can convey important contextual information for M3L. As such, they may have a compromised performance.\
 In this paper, we propose a collaborative matrix factorization based solution called M3Lcmf. M3Lcmf first uses a heterogeneous network composed of nodes of bags, instances, and labels, to encode different types of relations via multiple relational data matrices. To preserve the intrinsic structure of the data matrices, M3Lcmf collaboratively factorizes them into low-rank matrices, explores the latent relationships between bags, instances, and labels, and selectively merges the data matrices. An aggregation scheme is further introduced to aggregate the instance-level labels into bag-level and to guide the factorization. An empirical study on benchmark datasets show that M3Lcmf outperforms other related competitive solutions both in the instance-level and bag-level prediction.