Pingjian Ding

Collective Prediction of Disease-Associated miRNAs Based on Transduction Learning

The discovery of human disease-related miRNA is a challenging problem for complex disease biology research. For existing computational methods, it is difficult to achieve excellent performance with sparse known miRNA-disease association verified by biological experiment. Here, we develop CPTL, a Collective Prediction based on Transduction Learning, to systematically prioritize miRNAs related to disease. By combining disease similarity, miRNA similarity with known miRNA-disease association, we construct a miRNA-disease network for predicting miRNA-disease association. Then, CPTL calculates relevance score and updates the network structure iteratively, until a convergence criterion is reached. The relevance score of node including miRNA and disease is calculated by the use of transduction learning based on its neighbors. The network structure is updated using relevance score, which increases the weight of important links. To show the effectiveness of our method, we compared CPTL with existing methods based on HMDD datasets. Experimental results indicate that CPTL outperforms existing approaches in terms of AUC, precision, recall, and F1-score. Moreover, experiments performed with different number of iterations verify that CPTL has good convergence. Besides, it is analyzed that the varying of weighted parameters affect predicted results. Case study on breast cancer has further confirmed the identification ability of CPTL.

Predicting MicroRNA-Disease Associations Using Kronecker Regularized Least Squares Based on Heterogeneous Omics Data

MicroRNAs (miRNAs) play critical roles in many biological processes. Predicting the miRNA-disease associations will aid in deciphering the underlying pathogenesis of human polygenic diseases. However, existing in silico prediction methods typically utilize a single or limited data sources for disease-related miRNA prioritization and most of the methods are biased toward known miRNA-disease associations. Due to the insufficient number of experimentally validated interactions as well as no experimentally verified negative samples, obtaining remarkable performances is still challenging for these methods. In this paper, we present a semi-supervised method of Kronecker regularized least squares for predicting the potential or missing miRNA-disease associations (KRLSM). KRLSM integrates different omics data to assist various diseases or miRNAs with sparsely known associations to make predictions, and combines the disease space and miRNA space into a whole miRNA-disease space by Kronecker product. Finally, the semi-supervised classifier of regularized least squares is adopted to identify disease-related miRNAs. The experiment results demonstrate that the proposed method outperforms the other state-of-the-art approaches. In addition, case studies of several common diseases further indicate the effectiveness of KRLSM to identify potential miRNA-disease associations.

A graph regularized non-negative matrix factorization method for identifying microRNA-disease associations

Motivation MicroRNAs (miRNAs) play crucial roles in post-transcriptional regulations and various cellular processes. The identification of disease-related miRNAs provides great insights into the underlying pathogenesis of diseases at a system level. However, most existing computational approaches are biased towards known miRNA-disease associations, which is inappropriate for those new diseases or miRNAs without any known association information. Results In this study, we propose a new method with graph regularized non-negative matrix factorization in heterogeneous omics data, called GRNMF, to discover potential associations between miRNAs and diseases, especially for new diseases and miRNAs or those diseases and miRNAs with sparse known associations. First, we integrate the disease semantic information and miRNA functional information to estimate disease similarity and miRNA similarity, respectively. Considering that there is no available interaction observed for new diseases or miRNAs, a preprocessing step is developed to construct the interaction score profiles that will assist in prediction. Next, a graph regularized non-negative matrix factorization framework is utilized to simultaneously identify potential associations for all diseases. The results indicated that our proposed method can effectively prioritize disease-associated miRNAs with higher accuracy compared with other recent approaches. Moreover, case studies also demonstrated the effectiveness of GRNMF to infer unknown miRNA-disease associations for those novel diseases and miRNAs. Availability The code of GRNMF is freely available at https://github.com/XIAO-HN/GRNMF/. Supplementary information Supplementary data are available at Bioinformatics online.

Predicting MicroRNA-Disease Associations Using Network Topological Similarity Based on DeepWalk

Recently, increasing experimental studies have shown that microRNAs (miRNAs) involved in multiple physiological processes are connected with several complex human diseases. Identifying human disease-related miRNAs will be useful in uncovering novel prognostic markers for cancer. Currently, several computational approaches have been developed for miRNA-disease association prediction based on the integration of additional biological information of diseases and miRNAs, such as disease semantic similarity and miRNA functional similarity. However, these methods do not work well when this information is unavailable. In this paper, we present a similarity-based miRNA-disease prediction method that enhances the existing association discovery methods through a topology-based similarity measure. DeepWalk, a deep learning method, is utilized in this paper to calculate similarities within a miRNA-disease association network. It shows superior predictive performance for 22 complex diseases, with area under the ROC curve scores ranging from 0.805 to 0.937 by using five-fold cross-validation. In addition, case studies on breast cancer, lung cancer, and prostatic cancer further justify the use of our method to discover latent miRNA-disease pairs.

Prediction of microRNA–disease associations with a Kronecker kernel matrix dimension reduction model

Identifying the associations between human diseases and microRNAs is key to understanding pathogenicity mechanisms and important for uncovering novel prognostic markers. To date, a series of computational approaches have been developed for the prediction of disease–microRNA associations. However, these methods remain difficult to perform satisfactorily for diseases with a few known associated microRNAs. This study introduces a novel computational model, namely, the Kronecker kernel matrix dimension reduction (KMDR) model, for identifying potential microRNA–disease associations. This model combines microRNA space and disease space in a larger microRNA–disease space by using the Kronecker product or the Kronecker sum. The predictive performance of our proposed approach was evaluated and validated based on known association datasets. The experimental results show that KMDR achieves reliable prediction with an average AUC of 0.8320 for 22 complex diseases, which indeed outperforms other competitive methods. Moreover, case studies on kidney cancer, breast cancer, and esophageal cancer further demonstrate the applicability of our method in the identification of new disease–microRNA pairs. The source code of KMDR is freely available at https://github.com/ghli16/KMDR.

PCE-FR: A Novel Method for Identifying Overlapping Protein Complexes in Weighted Protein-Protein Interaction Networks Using Pseudo-Clique Extension Based on Fuzzy Relation

Identifying overlapping protein complexes in protein-protein interaction (PPI) networks can provide insight into cellular functional organization and thus elucidate underlying cellular mechanisms. Recently, various algorithms for protein complexes detection have been developed for PPI networks. However, majority of algorithms primarily depend on network topological feature and/or gene expression profile, failing to consider the inherent biological meanings between protein pairs. In this paper, we propose a novel method to detect protein complexes using pseudo-clique extension based on fuzzy relation (PCE-FR). Our algorithm operates in three stages: it first forms the nonoverlapping protein substructure based on fuzzy relation and then expands each substructure by adding neighbor proteins to maximize the cohesive score. Finally, highly overlapped candidate protein complexes are merged to form the final protein complex set. Particularly, our algorithm employs the biological significance hidden in protein pairs to construct edge weight for protein interaction networks. The experiment results show that our method can not only outperform classical algorithms such as CFinder, ClusterONE, CMC, RRW, HC-PIN, and ProRank +, but also achieve ideal overall performance in most of the yeast PPI datasets in terms of composite score consisting of precision, accuracy, and separation. We further apply our method to a human PPI network from the HPRD dataset and demonstrate it is very effective in detecting protein complexes compared to other algorithms.

Human disease MiRNA inference by combining target information based on heterogeneous manifolds

The emergence of network medicine has provided great insight into the identification of disease-related molecules, which could help with the development of personalized medicine. However, the state-of-the-art methods could neither simultaneously consider target information and the known miRNA-disease associations nor effectively explore novel gene-disease associations as a by-product during the process of inferring disease-related miRNAs. Computational methods incorporating multiple sources of information offer more opportunities to infer disease-related molecules, including miRNAs and genes in heterogeneous networks at a system level. In this study, we developed a novel algorithm, named inference of Disease-related MiRNAs based on Heterogeneous Manifold (DMHM), to accurately and efficiently identify miRNA-disease associations by integrating multi-omics data. Graph-based regularization was utilized to obtain a smooth function on the data manifold, which constitutes the main principle of DMHM. The novelty of this framework lies in the relatedness between diseases and miRNAs, which are measured via heterogeneous manifolds on heterogeneous networks integrating target information. To demonstrate the effectiveness of DMHM, we conducted comprehensive experiments based on HMDD datasets and compared DMHM with six state-of-the-art methods. Experimental results indicated that DMHM significantly outperformed the other six methods under fivefold cross validation and de novo prediction tests. Case studies have further confirmed the practical usefulness of DMHM.

Predicting microRNA-disease associations using label propagation based on linear neighborhood similarity

Interactions between microRNAs (miRNAs) and diseases can yield important information for uncovering novel prognostic markers. Since experimental determination of disease-miRNA associations is time-consuming and costly, attention has been given to designing efficient and robust computational techniques for identifying undiscovered interactions. In this study, we present a label propagation model with linear neighborhood similarity, called LPLNS, to predict unobserved miRNA-disease associations. Additionally, a preprocessing step is performed to derive new interaction likelihood profiles that will contribute to the prediction since new miRNAs and diseases lack known associations. Our results demonstrate that the LPLNS model based on the known disease-miRNA associations could achieve impressive performance with an AUC of 0.9034. Furthermore, we observed that the LPLNS model based on new interaction likelihood profiles could improve the performance to an AUC of 0.9127. This was better than other comparable methods. In addition, case studies also demonstrated our methods outstanding performance for inferring undiscovered interactions between miRNAs and diseases, especially for novel diseases.

A Novel Group Wise-Based Method for Calculating Human miRNA Functional Similarity

The measurement of human miRNA functional similarity is an important research for studying miRNA-related therapeutic strategy. Pair wise-based approaches using disease-miRNA associations have recently become a popular tool for inferring miRNA functional similarity. However, the miRNA functional similarity is vitally influenced by calculation of the disease semantic similarity in those methods. Moreover, integrating information content with hierarchical structure can improve calculation of the miRNA functional similarity. Therefore, we propose a group-wise method for inferring the miRNA functional similarity, named GMFS. First, the information content is computed by using disease MeSH descriptors to describe the specific of disease. Second, the acquirement of disease feature is based on the hierarchical structure as well as the information content of disease. Finally, the miRNA functional similarity is measured by using both miRNA-disease associations and the disease feature. To validate the effectiveness of the GMFS, we compare our method with several existing methods in terms of the average similarity of intra-family, inter-family, intra-cluster, and inter-cluster groups. The

GRTR: Drug-Disease Association Prediction Based on Graph Regularized Transductive Regression on Heterogeneous Network

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