A novel information diffusion method based on network consistency for identifying disease related microRNAs

SCPLPA: An miRNA-disease association prediction model based on spatial consistency projection and label propagation algorithm

Identifying the association between miRNA and diseases is helpful for disease prevention, diagnosis and treatment. It is of great significance to use computational methods to predict potential human miRNA disease associations. Considering the shortcomings of existing computational methods, such as low prediction accuracy and weak generalization, we propose a new method called SCPLPA to predict miRNA-disease associations. First, a heterogeneous disease similarity network was constructed using the disease semantic similarity network and the disease Gaussian interaction spectrum kernel similarity network, while a heterogeneous miRNA similarity network was constructed using the miRNA functional similarity network and the miRNA Gaussian interaction spectrum kernel similarity network. Then, the estimated miRNA-disease association scores were evaluated by integrating the outcomes obtained by implementing label propagation algorithms in the heterogeneous disease similarity network and the heterogeneous miRNA similarity network. Finally, the spatial consistency projection algorithm of the network was used to extract miRNA disease association features to predict unverified associations between miRNA and diseases. SCPLPA was compared with four classical methods (MDHGI, NSEMDA, RFMDA and SNMFMDA), and the results of multiple evaluation metrics showed that SCPLPA exhibited the most outstanding predictive performance. Case studies have shown that SCPLPA can effectively identify miRNAs associated with colon neoplasms and kidney neoplasms. In summary, our proposed SCPLPA algorithm is easy to implement and can effectively predict miRNA disease associations, making it a reliable auxiliary tool for biomedical research.

Empowering Graph Neural Networks with Block-Based Dual Adaptive Deep Adjustment for Drug Resistance-Related NcRNA Discovery

Drug resistance to chemotherapeutic agents remains a formidable challenge in cancer treatment, significantly impacting treatment efficacy. Extensive research has exposed the intimate involvement of noncoding RNAs (ncRNAs) in conferring resistance to cancer drugs. Understanding the intricate associations between ncRNAs and drug resistance is of pivotal importance in advancing clinical interventions and expediting drug development. However, traditional biological experimental methods are hampered by limitations, such as labor intensiveness, time consumption, and constraints in scalability. Addressing these challenges necessitates the development of efficient computational methods for the accurate prediction of potential ncRNA-drug resistance associations (NDRA). However, most existing predictive models primarily focus on known ncRNA-drug resistance associations, often neglecting the critical aspect of similarity information between ncRNAs and drug resistance. This oversight may hinder the accuracy of characterizing these associations. To overcome the limitations of existing computational models, we proposed B-NDRA, a computational framework designed for the discovery of drug resistance-related ncRNA. Initially, we constructed a heterogeneous graph that integrates ncRNA-drug resistance pairs, leveraging both known associations and similarity fusion information between ncRNAs and drug resistance. Subsequently, we employed an attention mechanism to aggregate local features of graph nodes following a dimensionality reduction of node features. Further, a graph neural network (GNN) facilitated the learning of global node embeddings. Notably, the integration of dual adaptive deep adjustment architectures, encompassing intrablock and interblock methodologies, enabled efficient extraction of global features while balancing local and global features. Finally, B-NDRA employed a multilayer perceptron to predict associations between ncRNAs and drug resistance. Through rigorous 5-fold cross-validation, B-NDRA achieved average AUC, AUPR, Accuracy, Precision, Recall, and F1-score values of 92.2%, 91.9%, 84.88%, 86.9%, 82.37%, and 84.44%, respectively. Furthermore, comparative evaluations were conducted on established models, namely, GAEMDA, GRPAMDA, and LRGCPND. The results, obtained through three distinct 5-fold cross-validation strategies, demonstrated a notable performance improvement across almost all metrics for our B-NDRA. Specific case studies targeting Doxorubicin and Imatinib further validated the practicality of our B-NDRA in discovering potential NDRA. These results confirm the potential of our B-NDRA as a valuable tool in advancing cancer research and therapeutic development. The source code and data set of B-NDRA can be found at https://github.com/XuanLi1145/B-NDRA.

KATZNCP: a miRNA-disease association prediction model integrating KATZ algorithm and network consistency projection

BACKGROUND

Clinical studies have shown that miRNAs are closely related to human health. The study of potential associations between miRNAs and diseases will contribute to a profound understanding of the mechanism of disease development, as well as human disease prevention and treatment. MiRNA-disease associations predicted by computational methods are the best complement to biological experiments.

RESULTS

In this research, a federated computational model KATZNCP was proposed on the basis of the KATZ algorithm and network consistency projection to infer the potential miRNA-disease associations. In KATZNCP, a heterogeneous network was initially constructed by integrating the known miRNA-disease association, integrated miRNA similarities, and integrated disease similarities; then, the KATZ algorithm was implemented in the heterogeneous network to obtain the estimated miRNA-disease prediction scores. Finally, the precise scores were obtained by the network consistency projection method as the final prediction results. KATZNCP achieved the reliable predictive performance in leave-one-out cross-validation (LOOCV) with an AUC value of 0.9325, which was better than the state-of-the-art comparable algorithms. Furthermore, case studies of lung neoplasms and esophageal neoplasms demonstrated the excellent predictive performance of KATZNCP.

CONCLUSION

A new computational model KATZNCP was proposed for predicting potential miRNA-drug associations based on KATZ and network consistency projections, which can effectively predict the potential miRNA-disease interactions. Therefore, KATZNCP can be used to provide guidance for future experiments.

Updated review of advances in microRNAs and complex diseases: towards systematic evaluation of computational models

Currently, there exist no generally accepted strategies of evaluating computational models for microRNA-disease associations (MDAs). Though K-fold cross validations and case studies seem to be must-have procedures, the value of K, the evaluation metrics, and the choice of query diseases as well as the inclusion of other procedures (such as parameter sensitivity tests, ablation studies and computational cost reports) are all determined on a case-by-case basis and depending on the researchers' choices. In the current review, we include a comprehensive analysis on how 29 state-of-the-art models for predicting MDAs were evaluated. Based on the analytical results, we recommend a feasible evaluation workflow that would suit any future model to facilitate fair and systematic assessment of predictive performance.

Using Graph Attention Network and Graph Convolutional Network to Explore Human CircRNA-Disease Associations Based on Multi-Source Data

Cumulative research studies have verified that multiple circRNAs are closely associated with the pathogenic mechanism and cellular level. Exploring human circRNA-disease relationships is significant to decipher pathogenic mechanisms and provide treatment plans. At present, several computational models are designed to infer potential relationships between diseases and circRNAs. However, the majority of existing approaches could not effectively utilize the multisource data and achieve poor performance in sparse networks. In this study, we develop an advanced method, GATGCN, using graph attention network (GAT) and graph convolutional network (GCN) to detect potential circRNA-disease relationships. First, several sources of biomedical information are fused via the centered kernel alignment model (CKA), which calculates the corresponding weight of different kernels. Second, we adopt the graph attention network to learn latent representation of diseases and circRNAs. Third, the graph convolutional network is deployed to effectively extract features of associations by aggregating feature vectors of neighbors. Meanwhile, GATGCN achieves the prominent AUC of 0.951 under leave-one-out cross-validation and AUC of 0.932 under 5-fold cross-validation. Furthermore, case studies on lung cancer, diabetes retinopathy, and prostate cancer verify the reliability of GATGCN for detecting latent circRNA-disease pairs.

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.

A novel miRNA-disease association prediction model using dual random walk with restart and space projection federated method

A large number of studies have shown that the variation and disorder of miRNAs are important causes of diseases. The recognition of disease-related miRNAs has become an important topic in the field of biological research. However, the identification of disease-related miRNAs by biological experiments is expensive and time consuming. Thus, computational prediction models that predict disease-related miRNAs must be developed. A novel network projection-based dual random walk with restart (NPRWR) was used to predict potential disease-related miRNAs. The NPRWR model aims to estimate and accurately predict miRNA-disease associations by using dual random walk with restart and network projection technology, respectively. The leave-one-out cross validation (LOOCV) was adopted to evaluate the prediction performance of NPRWR. The results show that the area under the receiver operating characteristic curve(AUC) of NPRWR was 0.9029, which is superior to that of other advanced miRNA-disease associated prediction methods. In addition, lung and kidney neoplasms were selected to present a case study. Among the first 50 miRNAs predicted, 50 and 49 miRNAs have been proven by in databases or relevant literature. Moreover, NPRWR can be used to predict isolated diseases and new miRNAs. LOOCV and the case study achieved good prediction results. Thus, NPRWR will become an effective and accurate disease-miRNA association prediction model.

NMCMDA: neural multicategory MiRNA-disease association prediction

MOTIVATION

There is growing evidence showing that the dysregulations of miRNAs cause diseases through various kinds of the underlying mechanism. Thus, predicting the multiple-category associations between microRNAs (miRNAs) and diseases plays an important role in investigating the roles of miRNAs in diseases. Moreover, in contrast with traditional biological experiments which are time-consuming and expensive, computational approaches for the prediction of multicategory miRNA-disease associations are time-saving and cost-effective that are highly desired for us.

RESULTS

We present a novel data-driven end-to-end learning-based method of neural multiple-category miRNA-disease association prediction (NMCMDA) for predicting multiple-category miRNA-disease associations. The NMCMDA has two main components: (i) encoder operates directly on the miRNA-disease heterogeneous network and leverages Graph Neural Network to learn miRNA and disease latent representations, respectively. (ii) Decoder yields miRNA-disease association scores with the learned latent representations as input. Various kinds of encoders and decoders are proposed for NMCMDA. Finally, the NMCMDA with the encoder of Relational Graph Convolutional Network and the neural multirelational decoder (NMR-RGCN) achieves the best prediction performance. We compared the NMCMDA with other baselines on three experimental datasets. The experimental results show that the NMR-RGCN is significantly superior to the state-of-the-art method TDRC in terms of Top-1 precision, Top-1 Recall, and Top-1 F1. Additionally, case studies are provided for two high-risk human diseases (namely, breast cancer and lung cancer) and we also provide the prediction and validation of top-10 miRNA-disease-category associations based on all known data of HMDD v3.2, which further validate the effectiveness and feasibility of the proposed method.

MSFSP: A Novel miRNA-Disease Association Prediction Model by Federating Multiple-Similarities Fusion and Space Projection

Growing evidences have indicated that microRNAs (miRNAs) play a significant role relating to many important bioprocesses; their mutations and disorders will cause the occurrence of various complex diseases. The prediction of miRNAs associated with underlying diseases via computational approaches is beneficial to identify biomarkers and discover specific medicine, which can greatly reduce the cost of diagnosis, cure, prognosis, and prevention of human diseases. However, how to further achieve a more reliable prediction of potential miRNA-disease associations with effective integration of different biological data is a challenge for researchers. In this study, we proposed a computational model by using a federated method of combined multiple-similarities fusion and space projection (MSFSP). MSFSP firstly fused the integrated disease similarity (composed of disease semantic similarity, disease functional similarity, and disease Hamming similarity) with the integrated miRNA similarity (composed of miRNA functional similarity, miRNA sequence similarity, and miRNA Hamming similarity). Secondly, it constructed the weighted network of miRNA-disease associations from the experimentally verified Boolean network of miRNA-disease associations by using similarity networks. Finally, it calculated the prediction results by weighting miRNA space projection scores and the disease space projection scores. Leave-one-out cross-validation demonstrated that MSFSP has the distinguished predictive accuracy with area under the receiver operating characteristics curve (AUC) of 0.9613 better than that of five other existing models. In case studies, the predictive ability of MSFSP was further confirmed as 96 and 98% of the top 50 predictions for prostatic neoplasms and lung neoplasms were successfully validated by experimental evidences and supporting experimental evidences were also found for 100% of the top 50 predictions for isolated diseases.

LDAI-ISPS: LncRNA-Disease Associations Inference Based on Integrated Space Projection Scores

Long non-coding RNAs (long ncRNAs, lncRNAs) of all kinds have been implicated in a range of cell developmental processes and diseases, while they are not translated into proteins. Inferring diseases associated lncRNAs by computational methods can be helpful to understand the pathogenesis of diseases, but those current computational methods still have not achieved remarkable predictive performance: such as the inaccurate construction of similarity networks and inadequate numbers of known lncRNA-disease associations. In this research, we proposed a lncRNA-disease associations inference based on integrated space projection scores (LDAI-ISPS) composed of the following key steps: changing the Boolean network of known lncRNA-disease associations into the weighted networks via combining all the global information (e.g., disease semantic similarities, lncRNA functional similarities, and known lncRNA-disease associations); obtaining the space projection scores via vector projections of the weighted networks to form the final prediction scores without biases. The leave-one-out cross validation (LOOCV) results showed that, compared with other methods, LDAI-ISPS had a higher accuracy with area-under-the-curve (AUC) value of 0.9154 for inferring diseases, with AUC value of 0.8865 for inferring new lncRNAs (whose associations related to diseases are unknown), with AUC value of 0.7518 for inferring isolated diseases (whose associations related to lncRNAs are unknown). A case study also confirmed the predictive performance of LDAI-ISPS as a helper for traditional biological experiments in inferring the potential LncRNA-disease associations and isolated diseases.

Neural inductive matrix completion with graph convolutional networks for miRNA-disease association prediction

Motivation

Predicting the association between microRNAs (miRNAs) and diseases plays an import role in identifying human disease-related miRNAs. As identification of miRNA-disease associations via biological experiments is time-consuming and expensive, computational methods are currently used as effective complements to determine the potential associations between disease and miRNA.

Results

We present a novel method of neural inductive matrix completion with graph convolutional network (NIMCGCN) for predicting miRNA-disease association. NIMCGCN first uses graph convolutional networks to learn miRNA and disease latent feature representations from the miRNA and disease similarity networks. Then, learned features were input into a novel neural inductive matrix completion (NIMC) model to generate an association matrix completion. The parameters of NIMCGCN were learned based on the known miRNA-disease association data in a supervised end-to-end way. We compared the proposed method with other state-of-the-art methods. The area under the receiver operating characteristic curve results showed that our method is significantly superior to existing methods. Furthermore, 50, 47 and 48 of the top 50 predicted miRNAs for three high-risk human diseases, namely, colon cancer, lymphoma and kidney cancer, were verified using experimental literature. Finally, 100% prediction accuracy was achieved when breast cancer was used as a case study to evaluate the ability of NIMCGCN for predicting a new disease without any known related miRNAs.

Availability and implementation

https://github.com/ljatynu/NIMCGCN/

Supplementary information

Supplementary data are available at Bioinformatics online.

Bipartite Heterogeneous Network Method Based on Co-neighbor for MiRNA-Disease Association Prediction

In recent years, miRNA variation and dysregulation have been found to be closely related to human tumors, and identifying miRNA-disease associations is helpful for understanding the mechanisms of disease or tumor development and is greatly significant for the prognosis, diagnosis, and treatment of human diseases. This article proposes a Bipartite Heterogeneous network link prediction method based on co-neighbor to predict miRNA-disease association (BHCN). According to the structural characteristics of the bipartite network, the concept of bipartite network co-neighbors is proposed, and the co-neighbors were used to represent the probability of association between disease and miRNA. To predict the isolated diseases and the new miRNA based on the association probability expressed by co-neighbors, we utilized the similarity between disease nodes and the similarity between miRNA nodes in heterogeneous networks to represent the association probability between disease and miRNA. The model's predictive performance was evaluated by the leave-one-out cross validation (LOOCV) on different datasets. The AUC value of BHCN on the gold benchmark dataset was 0.7973, and the AUC obtained on the prediction dataset was 0.9349, which was better than that of the classic global algorithm. In this case study, we conducted predictive studies on breast neoplasms and colon neoplasms. Most of the top 50 predicted results were confirmed by three databases, namely, HMDD, miR2disease, and dbDEMC, with accuracy rates of 96 and 82%. In addition, BHCN can be used for predicting isolated diseases (without any known associated diseases) and new miRNAs (without any known associated miRNAs). In the isolated disease case study, the top 50 of breast neoplasm and colon neoplasm potentials associated with miRNAs predicted an accuracy of 100 and 96%, respectively, thereby demonstrating the favorable predictive power of BHCN for potentially relevant miRNAs.

Identifying Disease-Gene Associations With Graph-Regularized Manifold Learning

Complex diseases are known to be associated with disease genes. Uncovering disease-gene associations is critical for diagnosis, treatment, and prevention of diseases. Computational algorithms which effectively predict candidate disease-gene associations prior to experimental proof can greatly reduce the associated cost and time. Most existing methods are disease-specific which can only predict genes associated with a specific disease at a time. Similarities among diseases are not used during the prediction. Meanwhile, most methods predict new disease genes based on known associations, making them unable to predict disease genes for diseases without known associated genes.In this study, a manifold learning-based method is proposed for predicting disease-gene associations by assuming that the geodesic distance between any disease and its associated genes should be shorter than that of other non-associated disease-gene pairs. The model maps the diseases and genes into a lower dimensional manifold based on the known disease-gene associations, disease similarity and gene similarity to predict new associations in terms of the geodesic distance between disease-gene pairs. In the 3-fold cross-validation experiments, our method achieves scores of 0.882 and 0.854 in terms of the area under of the receiver operating characteristic (ROC) curve (AUC) for diseases with more than one known associated genes and diseases with only one known associated gene, respectively. Further de novo studies on Lung Cancer and Bladder Cancer also show that our model is capable of identifying new disease-gene associations.

LSGSP: a novel miRNA–disease association prediction model using a Laplacian score of the graphs and space projection federated method

Lots of research findings have indicated that miRNAs (microRNAs) are involved in many important biological processes; their mutations and disorders are closely related to diseases, therefore, determining the associations between human diseases and miRNAs is key to understand pathogenic mechanisms. Existing biological experimental methods for identifying miRNA–disease associations are usually expensive and time consuming. Therefore, the development of efficient and reliable computational methods for identifying disease-related miRNAs has become an important topic in the field of biological research in recent years. In this study, we developed a novel miRNA–disease association prediction model using a Laplacian score of the graphs and space projection federated method (LSGSP). This integrates experimentally validated miRNA–disease associations, disease semantic similarity scores, miRNA functional scores, and miRNA family information to build a new disease similarity network and miRNA similarity network, and then obtains the global similarities of these networks through calculating the Laplacian score of the graphs, based on which the miRNA–disease weighted network can be constructed through combination with the miRNA–disease Boolean network. Finally, the miRNA–disease score was obtained via projecting the miRNA space and disease space onto the miRNA–disease weighted network. Compared with several other state-of-the-art methods, using leave-one-out cross validation (LOOCV) to evaluate the accuracy of LSGSP with respect to a benchmark dataset, prediction dataset and compare dataset, LSGSP showed excellent predictive performance with high AUC values of 0.9221, 0.9745 and 0.9194, respectively. In addition, for prostate neoplasms and lung neoplasms, the consistencies between the top 50 predicted miRNAs (obtained from LSGSP) and the results (confirmed from the updated HMDD, miR2Disease, and dbDEMC databases) reached 96% and 100%, respectively. Similarly, for isolated diseases (diseases not associated with any miRNAs), the consistencies between the top 50 predicted miRNAs (obtained from LSGSP) and the results (confirmed from the above-mentioned three databases) reached 98% and 100%, respectively. These results further indicate that LSGSP can effectively predict potential associations between miRNAs and diseases.

Lots of research findings have indicated that the mutations and disorders of miRNAs (microRNAs) are closely related to diseases. Therefore, determining the associations between human diseases and miRNAs is key to understand the pathogenic mechanisms.