MLRDFM: a multi-view Laplacian regularized DeepFM model for predicting miRNA-disease associations

MOTIVATION

MicroRNAs (miRNAs), as critical regulators, are involved in various fundamental and vital biological processes, and their abnormalities are closely related to human diseases. Predicting disease-related miRNAs is beneficial to uncovering new biomarkers for the prevention, detection, prognosis, diagnosis and treatment of complex diseases.

RESULTS

In this study, we propose a multi-view Laplacian regularized deep factorization machine (DeepFM) model, MLRDFM, to predict novel miRNA-disease associations while improving the standard DeepFM. Specifically, MLRDFM improves DeepFM from two aspects: first, MLRDFM takes the relationships among items into consideration by regularizing their embedding features via their similarity-based Laplacians. In this study, miRNA Laplacian regularization integrates four types of miRNA similarity, while disease Laplacian regularization integrates two types of disease similarity. Second, to judiciously train our model, Laplacian eigenmaps are utilized to initialize the weights in the dense embedding layer. The experimental results on the latest HMDD v3.2 dataset show that MLRDFM improves the performance and reduces the overfitting phenomenon of DeepFM. Besides, MLRDFM is greatly superior to the state-of-the-art models in miRNA-disease association prediction in terms of different evaluation metrics with the 5-fold cross-validation. Furthermore, case studies further demonstrate the effectiveness of MLRDFM.

TSMDA: Target and symptom-based computational model for miRNA-disease-association prediction

The emergence of high-throughput sequencing techniques has revealed a primary role of microRNAs (miRNAs) in a wide range of diseases, including cancers and neurodegenerative disorders. Understanding novel relationships between miRNAs and diseases can potentially unveil complex pathogenesis mechanisms, leading to effective diagnosis and treatment. The investigation of novel miRNA-disease associations, however, is currently costly and time consuming. Over the years, several computational models have been proposed to prioritize potential miRNA-disease associations, but with limited usability or predictive capability. In order to fill this gap, we introduce TSMDA, a novel machine-learning method that leverages target and symptom information and negative sample selection to predict miRNA-disease association. TSMDA significantly outperforms similar methods, achieving an area under the receiver operating characteristic (ROC) curve (AUC) of 0.989 and 0.982 under 5-fold cross-validation and blind test, respectively. We also demonstrate the capability of the method to uncover potential miRNA-disease associations in breast, prostate, and lung cancers, as case studies. We believe TSMDA will be an invaluable tool for the community to explore and prioritize potentially new miRNA-disease associations for further experimental characterization. The method was made available as a freely accessible and user-friendly web interface at http://biosig.unimelb.edu.au/tsmda/.

Integration of pairwise neighbor topologies and miRNA family and cluster attributes for miRNA-disease association prediction

Identifying disease-related microRNAs (miRNAs) assists the understanding of disease pathogenesis. Existing research methods integrate multiple kinds of data related to miRNAs and diseases to infer candidate disease-related miRNAs. The attributes of miRNA nodes including their family and cluster belonging information, however, have not been deeply integrated. Besides, the learning of neighbor topology representation of a pair of miRNA and disease is a challenging issue. We present a disease-related miRNA prediction method by encoding and integrating multiple representations of miRNA and disease nodes learnt from the generative and adversarial perspective. We firstly construct a bilayer heterogeneous network of miRNA and disease nodes, and it contains multiple types of connections among these nodes, which reflect neighbor topology of miRNA-disease pairs, and the attributes of miRNA nodes, especially miRNA-related families and clusters. To learn enhanced pairwise neighbor topology, we propose a generative and adversarial model with a convolutional autoencoder-based generator to encode the low-dimensional topological representation of the miRNA-disease pair and multi-layer convolutional neural network-based discriminator to discriminate between the true and false neighbor topology embeddings. Besides, we design a novel feature category-level attention mechanism to learn the various importance of different features for final adaptive fusion and prediction. Comparison results with five miRNA-disease association methods demonstrated the superior performance of our model and technical contributions in terms of area under the receiver operating characteristic curve and area under the precision-recall curve. The results of recall rates confirmed that our model can find more actual miRNA-disease associations among top-ranked candidates. Case studies on three cancers further proved the ability to detect potential candidate miRNAs.

Efficient framework for predicting MiRNA-disease associations based on improved hybrid collaborative filtering

BACKGROUND

Accumulating studies indicates that microRNAs (miRNAs) play vital roles in the process of development and progression of many human complex diseases. However, traditional biochemical experimental methods for identifying disease-related miRNAs cost large amount of time, manpower, material and financial resources.

METHODS

In this study, we developed a framework named hybrid collaborative filtering for miRNA-disease association prediction (HCFMDA) by integrating heterogeneous data, e.g., miRNA functional similarity, disease semantic similarity, known miRNA-disease association networks, and Gaussian kernel similarity of miRNAs and diseases. To capture the intrinsic interaction patterns embedded in the sparse association matrix, we prioritized the predictive score by fusing three types of information: similar disease associations, similar miRNA associations, and similar disease-miRNA associations. Meanwhile, singular value decomposition was adopted to reduce the impact of noise and accelerate predictive speed.

RESULTS

We then validated HCFMDA with leave-one-out cross-validation (LOOCV) and two types of case studies. In the LOOCV, we achieved 0.8379 of AUC (area under the curve). To evaluate the performance of HCFMDA on real diseases, we further implemented the first type of case validation over three important human diseases: Colon Neoplasms, Esophageal Neoplasms and Prostate Neoplasms. As a result, 44, 46 and 44 out of the top 50 predicted disease-related miRNAs were confirmed by experimental evidence. Moreover, the second type of case validation on Breast Neoplasms indicates that HCFMDA could also be applied to predict potential miRNAs towards those diseases without any known associated miRNA.

CONCLUSIONS

The satisfactory prediction performance demonstrates that our model could serve as a reliable tool to guide the following research for identifying candidate miRNAs associated with human diseases.

MvKFN-MDA: Multi-view Kernel Fusion Network for miRNA-disease association prediction

Predicting the associations between microRNAs (miRNAs) and diseases is of great significance for identifying miRNAs related to human diseases. Since it is time-consuming and costly to identify the association between miRNA and disease through biological experiments, computational methods are currently used as an effective supplement to identify the potential association between disease and miRNA. This paper presents a Multi-view Kernel Fusion Network (MvKFN) based prediction method (MvKFN-MDA) to address the problem of miRNA-disease associations prediction. A novel multiple kernel fusion framework Multi-view Kernel Fusion Network (MvKFN) is first proposed to effectively fuse different views similarity kernels constructed from different data sources in a highly nonlinear way. Using MvKFNs, both different base similarity kernels for miRNA, such as sequence, functional, semantic, Gaussian profile kernels and different base similarity kernels for diseases, such as semantic, Gaussian profile kernel are nonlinearly fused into two integrated similarity kernels, one for miRNA, another for disease. Then, miRNA and disease feature representations are extracted from the miRNA and disease integrated similarity kernels respectively. These features are then fed into a neural matrix completion framework which finally outputs the association prediction scores. The parameters of MvKFN-MDA are learned based on the known miRNA-disease association matrix in a supervised end-to-end way. We compare the proposed method with other state-of-the-art methods. The AUCs of our proposed method were superior to the existing methods in both 5-FCV and LOOCV on two open experimental datasets. Furthermore, 49, 48, and 47 of the top 50 predicted miRNAs for three high-risk human diseases, namely, colon cancer, lymphoma, and kidney cancer, are verified respectively using experimental literature. Finally, 100% accuracy from the top 50 predicted miRNAs is achieved when breast cancer is used as a case study to evaluate the ability of MvKFN-MDA for predicting a new disease without any known related miRNAs.

Predicting miRNA-Disease Association Based on Modularity Preserving Heterogeneous Network Embedding

MicroRNAs (miRNAs) are a category of small non-coding RNAs that profoundly impact various biological processes related to human disease. Inferring the potential miRNA-disease associations benefits the study of human diseases, such as disease prevention, disease diagnosis, and drug development. In this work, we propose a novel heterogeneous network embedding-based method called MDN-NMTF (Module-based Dynamic Neighborhood Non-negative Matrix Tri-Factorization) for predicting miRNA-disease associations. MDN-NMTF constructs a heterogeneous network of disease similarity network, miRNA similarity network and a known miRNA-disease association network. After that, it learns the latent vector representation for miRNAs and diseases in the heterogeneous network. Finally, the association probability is computed by the product of the latent miRNA and disease vectors. MDN-NMTF not only successfully integrates diverse biological information of miRNAs and diseases to predict miRNA-disease associations, but also considers the module properties of miRNAs and diseases in the course of learning vector representation, which can maximally preserve the heterogeneous network structural information and the network properties. At the same time, we also extend MDN-NMTF to a new version (called MDN-NMTF2) by using modular information to improve the miRNA-disease association prediction ability. Our methods and the other four existing methods are applied to predict miRNA-disease associations in four databases. The prediction results show that our methods can improve the miRNA-disease association prediction to a high level compared with the four existing methods.

Three-Layer Heterogeneous Network Combined With Unbalanced Random Walk for miRNA-Disease Association Prediction

miRNA plays an important role in many biological processes, and increasing evidence shows that miRNAs are closely related to human diseases. Most existing miRNA-disease association prediction methods were only based on data related to miRNAs and diseases and failed to effectively use other existing biological data. However, experimentally verified miRNA-disease associations are limited, there are complex correlations between biological data. Therefore, we propose a novel Three-layer heterogeneous network Combined with unbalanced Random Walk for MiRNA-Disease Association prediction algorithm (TCRWMDA), which can effectively integrate multi-source association data. TCRWMDA based not only on the known miRNA-disease associations, also add the new priori information (lncRNA-miRNA and lncRNA-disease associations) to build a three-layer heterogeneous network, lncRNA was added as the transition path of the intermediate point to mine more effective information between networks. The AUC value obtained by the TCRWMDA algorithm on 5-fold cross validation is 0.9209, compared with other models based on the same similarity calculation method, TCRWMDA obtained better results. TCRWMDA was applied to the analysis of four types of cancer, the results proved that TCRWMDA is an effective tool to predict the potential miRNA-disease association. The source code and dataset of TCRWMDA are available at: https://github.com/ylm0505/TCRWMDA.

An improved random forest-based computational model for predicting novel miRNA-disease associations

Background

A large body of evidence shows that miRNA regulates the expression of its target genes at post-transcriptional level and the dysregulation of miRNA is related to many complex human diseases. Accurately discovering disease-related miRNAs is conductive to the exploring of the pathogenesis and treatment of diseases. However, because of the limitation of time-consuming and expensive experimental methods, predicting miRNA-disease associations by computational models has become a more economical and effective mean.

Results

Inspired by the work of predecessors, we proposed an improved computational model based on random forest (RF) for identifying miRNA-disease associations (IRFMDA). First, the integrated similarity of diseases and the integrated similarity of miRNAs were calculated by combining the semantic similarity and Gaussian interaction profile kernel (GIPK) similarity of diseases, the functional similarity and GIPK similarity of miRNAs, respectively. Then, the integrated similarity of diseases and the integrated similarity of miRNAs were combined to represent each miRNA-disease relationship pair. Next, the miRNA-disease relationship pairs contained in the HMDD (v2.0) database were considered positive samples, and the randomly constructed miRNA-disease relationship pairs not included in HMDD (v2.0) were considered negative samples. Next, the feature selection based on the variable importance score of RF was performed to choose more useful features to represent samples to optimize the model’s ability of inferring miRNA-disease associations. Finally, a RF regression model was trained on reduced sample space to score the unknown miRNA-disease associations. The AUCs of IRFMDA under local leave-one-out cross-validation (LOOCV), global LOOCV and 5-fold cross-validation achieved 0.8728, 0.9398 and 0.9363, which were better than several excellent models for predicting miRNA-disease associations. Moreover, case studies on oesophageal cancer, lymphoma and lung cancer showed that 94 (oesophageal cancer), 98 (lymphoma) and 100 (lung cancer) of the top 100 disease-associated miRNAs predicted by IRFMDA were supported by the experimental data in the dbDEMC (v2.0) database.

Conclusions

Cross-validation and case studies demonstrated that IRFMDA is an excellent miRNA-disease association prediction model, and can provide guidance and help for experimental studies on the regulatory mechanism of miRNAs in complex human diseases in the future.

Self-Weighted Multi-Kernel Multi-Label Learning for Potential miRNA-Disease Association Prediction

Researchers have realized that microRNAs (miRNAs) play significant roles in the pathogenesis of various diseases. Although many computational models have been proposed to predict the associations between miRNAs and diseases, prediction performance could still be improved. In this paper, we propose a novel self-weighted, multi-kernel, multi-label learning (SwMKML) method to predict disease-related miRNAs. SwMKML adaptively learns two optimal kernel matrices for both miRNAs and diseases from multiple kernels constructed from known miRNA-disease associations. Moreover, the miRNA-disease associations predicted from both spaces are updated simultaneously based on a multi-label framework. Compared with four state-of-the-art computational models, SwMKML achieved best results of 95.5%, 93.1%, and 84.1% in global leave-one-out cross-validation, 5-fold cross-validation, and overall prediction accuracy, respectively. A case study conducted on head and neck neoplasms further identified two potential prognostic biomarkers, hsa-mir-125b-1 and hsa-mir-125b-2, for the disease. SwMKML is freely available at Github, and we anticipate that it may become an effective tool for potential miRNA-disease association prediction.

Dual Laplacian regularized matrix completion for microRNA-disease associations prediction

Since lots of miRNA-disease associations have been verified, it is meaningful to discover more miRNA-disease associations for serving disease diagnosis and prevention of human complex diseases. However, it is not practical to identify potential associations using traditional biological experimental methods since the process is expensive and time consuming. Therefore, it is necessary to develop efficient computational methods to accomplish this task. In this work, we introduced a matrix completion model with dual Laplacian regularization (DLRMC) to infer unknown miRNA-disease associations in heterogeneous omics data. Specifically, DLRMC transformed the task of miRNA-disease association prediction into a matrix completion problem, in which the potential missing entries of the miRNA-disease association matrix were calculated, the missing association can be obtained based on the prediction scores after the completion procedure. Meanwhile, the miRNA functional similarity and the disease semantic similarity were fully exploited to serve the miRNA-disease association matrix completion by using a dual Laplacian regularization term. In the experiments, we conducted global and local Leave-One-Out Cross Validation (LOOCV) and case studies to evaluate the efficacy of DLRMC on the Human miRNA-disease associations dataset obtained from the HMDDv2.0 database. As a result, the AUCs of DLRMC is 0.9174 and 0.8289 in global LOOCV and local LOOCV, respectively, which significantly outperform a variety of previous methods. In addition, in the case studies on four significant diseases related to human health including Colon Neoplasms, Kidney neoplasms, Lymphoma and Prostate neoplasms, 90%, 92%, 92% and 94% out of the top 50 predicted miRNAs has been confirmed, respectively.

MCLPMDA: A novel method for miRNA-disease association prediction based on matrix completion and label propagation

MiRNAs are a class of small non‐coding RNAs that are involved in the development and progression of various complex diseases. Great efforts have been made to discover potential associations between miRNAs and diseases recently. As experimental methods are in general expensive and time‐consuming, a large number of computational models have been developed to effectively predict reliable disease‐related miRNAs. However, the inherent noise and incompleteness in the existing biological datasets have inevitably limited the prediction accuracy of current computational models. To solve this issue, in this paper, we propose a novel method for miRNA‐disease association prediction based on matrix completion and label propagation. Specifically, our method first reconstructs a new miRNA/disease similarity matrix by matrix completion algorithm based on known experimentally verified miRNA‐disease associations and then utilizes the label propagation algorithm to reliably predict disease‐related miRNAs. As a result, MCLPMDA achieved comparable performance under different evaluation metrics and was capable of discovering greater number of true miRNA‐disease associations. Moreover, case study conducted on Breast Neoplasms further confirmed the prediction reliability of the proposed method. Taken together, the experimental results clearly demonstrated that MCLPMDA can serve as an effective and reliable tool for miRNA‐disease association prediction.