A path-based measurement for human miRNA functional similarities using miRNA-disease associations

MGCNSS: miRNA-disease association prediction with multi-layer graph convolution and distance-based negative sample selection strategy

Identifying disease-associated microRNAs (miRNAs) could help understand the deep mechanism of diseases, which promotes the development of new medicine. Recently, network-based approaches have been widely proposed for inferring the potential associations between miRNAs and diseases. However, these approaches ignore the importance of different relations in meta-paths when learning the embeddings of miRNAs and diseases. Besides, they pay little attention to screening out reliable negative samples which is crucial for improving the prediction accuracy. In this study, we propose a novel approach named MGCNSS with the multi-layer graph convolution and high-quality negative sample selection strategy. Specifically, MGCNSS first constructs a comprehensive heterogeneous network by integrating miRNA and disease similarity networks coupled with their known association relationships. Then, we employ the multi-layer graph convolution to automatically capture the meta-path relations with different lengths in the heterogeneous network and learn the discriminative representations of miRNAs and diseases. After that, MGCNSS establishes a highly reliable negative sample set from the unlabeled sample set with the negative distance-based sample selection strategy. Finally, we train MGCNSS under an unsupervised learning manner and predict the potential associations between miRNAs and diseases. The experimental results fully demonstrate that MGCNSS outperforms all baseline methods on both balanced and imbalanced datasets. More importantly, we conduct case studies on colon neoplasms and esophageal neoplasms, further confirming the ability of MGCNSS to detect potential candidate miRNAs. The source code is publicly available on GitHub https://github.com/15136943622/MGCNSS/tree/master.

pPe Op inhibits HGC-27 cell proliferation, migration and invasion by upregulating miR-30b-5p and down-regulating the Rac1/Cdc42 pathway

Gastric cancer is the fifth most frequently occurring and the fourth most lethal malignant cancer worldwide. A bioactive protein (pPe Op) from Omphalia lapidescens exhibits significant inhibitory effects on gastric cancer cells. miRNA deep sequencing analysis shows that miR-30b-5p is significantly upregulated in HGC-27 cells treated with pPe Op. Verification results show that the expression level of miR-30b-5p is significantly increased in HGC-27 cells after pPe Op treatment. Additionally, miR-30b-5p is significantly downregulated in clinical gastric cancer tissues compared to that in adjacent normal tissues. Following pPe Op treatment and/or transfection with miR-30b-5p mimic, the proliferation, migration, and invasion of HGC-27 cells are significantly impaired. Immunofluorescence microscopy shows that pPe Op and/or miR-30b-5p destroy(s) microfilaments and microstructures and inhibit(s) the formation of pseudopodia. Bioinformatics analysis, dual-luciferase reporter assay, and western blot analysis confirm that miR-30b-5p downregulates Rac1/Cdc42 expression and activation by targeting RAB22A. Available data indicate that miR-30b-5p plays an anti-gastric cancer role in mediating pPe Op. pPe Op upregulates miR-30b-5p expression, which in turn inhibits RAB22A expression, resulting in a reduction in the expression and activation of Rac1 and Cdc42 and their downstream targets, thus destroying the cytoskeletal structure and inhibiting the proliferation, migration, and invasion of cancer cells.

RNADisease v4.0: an updated resource of RNA-associated diseases, providing RNA-disease analysis, enrichment and prediction

Numerous studies have shown that RNA plays an important role in the occurrence and development of diseases, and RNA-disease associations are not limited to noncoding RNAs in mammals but also exist for protein-coding RNAs. Furthermore, RNA-associated diseases are found across species including plants and nonmammals. To better analyze diseases at the RNA level and facilitate researchers in exploring the pathogenic mechanism of diseases, we decided to update and change MNDR v3.0 to RNADisease v4.0, a repository for RNA-disease association (http://www.rnadisease.org/ or http://www.rna-society.org/mndr/). Compared to the previous version, new features include: (i) expanded data sources and categories of species, RNA types, and diseases; (ii) the addition of a comprehensive analysis of RNAs from thousands of high-throughput sequencing data of cancer samples and normal samples; (iii) the addition of an RNA-disease enrichment tool and (iv) the addition of four RNA-disease prediction tools. In summary, RNADisease v4.0 provides a comprehensive and concise data resource of RNA-disease associations which contains a total of 3 428 058 RNA-disease entries covering 18 RNA types, 117 species and 4090 diseases to meet the needs of biological research and lay the foundation for future therapeutic applications of diseases.

Biomedical data, computational methods and tools for evaluating disease-disease associations

In recent decades, exploring potential relationships between diseases has been an active research field. With the rapid accumulation of disease-related biomedical data, a lot of computational methods and tools/platforms have been developed to reveal intrinsic relationship between diseases, which can provide useful insights to the study of complex diseases, e.g. understanding molecular mechanisms of diseases and discovering new treatment of diseases. Human complex diseases involve both external phenotypic abnormalities and complex internal molecular mechanisms in organisms. Computational methods with different types of biomedical data from phenotype to genotype can evaluate disease-disease associations at different levels, providing a comprehensive perspective for understanding diseases. In this review, available biomedical data and databases for evaluating disease-disease associations are first summarized. Then, existing computational methods for disease-disease associations are reviewed and classified into five groups in terms of the usages of biomedical data, including disease semantic-based, phenotype-based, function-based, representation learning-based and text mining-based methods. Further, we summarize software tools/platforms for computation and analysis of disease-disease associations. Finally, we give a discussion and summary on the research of disease-disease associations. This review provides a systematic overview for current disease association research, which could promote the development and applications of computational methods and tools/platforms for disease-disease associations.

Heterogeneous information network and its application to human health and disease

The molecular components with the functional interdependencies in human cell form complicated biological network. Diseases are mostly caused by the perturbations of the composite of the interaction multi-biomolecules, rather than an abnormality of a single biomolecule. Furthermore, new biological functions and processes could be revealed by discovering novel biological entity relationships. Hence, more and more biologists focus on studying the complex biological system instead of the individual biological components. The emergence of heterogeneous information network (HIN) offers a promising way to systematically explore complicated and heterogeneous relationships between various molecules for apparently distinct phenotypes. In this review, we first present the basic definition of HIN and the biological system considered as a complex HIN. Then, we discuss the topological properties of HIN and how these can be applied to detect network motif and functional module. Afterwards, methodologies of discovering relationships between disease and biomolecule are presented. Useful insights on how HIN aids in drug development and explores human interactome are provided. Finally, we analyze the challenges and opportunities for uncovering combinatorial patterns among pharmacogenomics and cell-type detection based on single-cell genomic data.

Inferring Synergistic Drug Combinations Based on Symmetric Meta-Path in a Novel Heterogeneous Network

Combinatorial drug therapy is a promising way for treating cancers, which can reduce drug side effects and improve drug efficacy. However, due to the large-scale combinatorial space, it is difficult to quickly and effectively identify novel synergistic drug combinations for further implementing combinatorial drug therapy. The computational method of fusing multi-source knowledge is a time- and cost-efficient strategy to infer synergistic drug combinations for testing. However, for the existing computational methods of inferring synergistic drug combinations, it still remains a challenging to effectively combine multi-source information to achieve the desired results. Hence, in this study, we developed a novel Inference method of Synergistic Drug Combinations based on Symmetric Meta-Path (ISDCSMP), which can systematically and accurately prioritize synergistic drug combinations in a novel drug-target heterogeneous network integrating multi-source information. In the experiment, ISDCSMP outperformed the state-of-the-art methods in terms of AUC and precision on the benchmark dataset in five-fold cross validation. Moreover, we further illustrated performances of different ways for obtaining the combination coefficients, and analyzed the influences of the maximum meta-path length. The performances of various single meta-paths were described in five-fold cross validation. Finally, we confirmed the practical usefulness of ISDCSMP with the predicted novel synergistic drug combinations. The source code of ISDCSMP is available at https://github.com/KDDing/ISDCSMP.

Fusing Multiple Biological Networks to Effectively Predict miRNA-disease Associations

Background:

MicroRNAs (miRNAs) are a class of endogenous non-coding RNAs with about 22 nucleotides, and they play a significant role in a variety of complex biological processes. Many researches have shown that miRNAs are closely related to human diseases. Although the biological experiments are reliable in identifying miRNA-disease associations, they are timeconsuming and costly.

Objective:

Thus, computational methods are urgently needed to effectively predict miRNA-disease associations.

Methods:

In this paper, we proposed a novel method, BIRWMDA, based on a bi-random walk model to predict miRNA-disease associations. Specifically, in BIRWMDA, the similarity network fusion algorithm is used to combine the multiple similarity matrices to obtain a miRNA-miRNA similarity matrix and a disease-disease similarity matrix, then the miRNA-disease associations were predicted by the bi-random walk model.

Results:

To evaluate the performance of BIRWMDA, we ran the leave-one-out cross-validation and 5-fold cross-validation, and their corresponding AUCs were 0.9303 and 0.9223 ± 0.00067, respectively. To further demonstrate the effectiveness of the BIRWMDA, from the perspective of exploring disease-related miRNAs, we conducted three case studies of breast neoplasms, prostate neoplasms and gastric neoplasms, where 48, 50 and 50 out of the top 50 predicted miRNAs were confirmed by literature, respectively. From the perspective of exploring miRNA-related diseases, we conducted two case studies of hsa-mir-21 and hsa-mir-155, where 7 and 5 out of the top 10 predicted diseases were confirmed by literatures, respectively.

Conclusion:

The fusion of multiple biological networks could effectively predict miRNA-diseases associations. We expected BIRWMDA to serve as a biological tool for mining potential miRNAdisease associations.

MDA-GCNFTG: identifying miRNA-disease associations based on graph convolutional networks via graph sampling through the feature and topology graph

Accurate identification of the miRNA-disease associations (MDAs) helps to understand the etiology and mechanisms of various diseases. However, the experimental methods are costly and time-consuming. Thus, it is urgent to develop computational methods towards the prediction of MDAs. Based on the graph theory, the MDA prediction is regarded as a node classification task in the present study. To solve this task, we propose a novel method MDA-GCNFTG, which predicts MDAs based on Graph Convolutional Networks (GCNs) via graph sampling through the Feature and Topology Graph to improve the training efficiency and accuracy. This method models both the potential connections of feature space and the structural relationships of MDA data. The nodes of the graphs are represented by the disease semantic similarity, miRNA functional similarity and Gaussian interaction profile kernel similarity. Moreover, we considered six tasks simultaneously on the MDA prediction problem at the first time, which ensure that under both balanced and unbalanced sample distribution, MDA-GCNFTG can predict not only new MDAs but also new diseases without known related miRNAs and new miRNAs without known related diseases. The results of 5-fold cross-validation show that the MDA-GCNFTG method has achieved satisfactory performance on all six tasks and is significantly superior to the classic machine learning methods and the state-of-the-art MDA prediction methods. Moreover, the effectiveness of GCNs via the graph sampling strategy and the feature and topology graph in MDA-GCNFTG has also been demonstrated. More importantly, case studies for two diseases and three miRNAs are conducted and achieved satisfactory performance.

MISIM v2.0: a web server for inferring microRNA functional similarity based on microRNA-disease associations

MicroRNAs (miRNAs) are one class of important small non-coding RNA molecules and play critical roles in health and disease. Therefore, it is important and necessary to evaluate the functional relationship of miRNAs and then predict novel miRNA-disease associations. For this purpose, here we developed the updated web server MISIM (miRNA similarity) v2.0. Besides a 3-fold increase in data content compared with MISIM v1.0, MISIM v2.0 improved the original MISIM algorithm by implementing both positive and negative miRNA-disease associations. That is, the MISIM v2.0 scores could be positive or negative, whereas MISIM v1.0 only produced positive scores. Moreover, MISIM v2.0 achieved an algorithm for novel miRNA-disease prediction based on MISIM v2.0 scores. Finally, MISIM v2.0 provided network visualization and functional enrichment analysis for functionally paired miRNAs. The MISIM v2.0 web server is freely accessible at http://www.lirmed.com/misim/.

MicroRNAs and complex diseases: from experimental results to computational models

Plenty of microRNAs (miRNAs) were discovered at a rapid pace in plants, green algae, viruses and animals. As one of the most important components in the cell, miRNAs play a growing important role in various essential and important biological processes. For the recent few decades, amounts of experimental methods and computational models have been designed and implemented to identify novel miRNA-disease associations. In this review, the functions of miRNAs, miRNA-target interactions, miRNA-disease associations and some important publicly available miRNA-related databases were discussed in detail. Specially, considering the important fact that an increasing number of miRNA-disease associations have been experimentally confirmed, we selected five important miRNA-related human diseases and five crucial disease-related miRNAs and provided corresponding introductions. Identifying disease-related miRNAs has become an important goal of biomedical research, which will accelerate the understanding of disease pathogenesis at the molecular level and molecular tools design for disease diagnosis, treatment and prevention. Computational models have become an important means for novel miRNA-disease association identification, which could select the most promising miRNA-disease pairs for experimental validation and significantly reduce the time and cost of the biological experiments. Here, we reviewed 20 state-of-the-art computational models of predicting miRNA-disease associations from different perspectives. Finally, we summarized four important factors for the difficulties of predicting potential disease-related miRNAs, the framework of constructing powerful computational models to predict potential miRNA-disease associations including five feasible and important research schemas, and future directions for further development of computational models.

A Novel Cluster-Based Computational Method to Identify miRNA Regulatory Modules

The identification of miRNA regulatory modules can help decipher miRNAs combinatorial regulation effects on the pathogenesis underlying complex diseases, especially in cancer. By integrating miRNA/mRNA expression profiles and sequence-based predicted target site information, we develop a novel cluster-based computational method named CoModule for identifying miRNA regulatory modules (MRMs). The ultimate goal of CoModule is to detect the MRMs, in which the miRNAs in each module are expected to present cooperative mechanisms in regulating their targets mRNAs. Here, the co-expression of miRNAs are believed to present cooperative regulatory relationship, therefore, the critical step of CoModule is first to partition the miRNAs with similar expression into a cluster by employing rough set clustering. After gaining credible miRNA clusters, the targets of regulator are naturally added into corresponding clusters to produce the final miRNA regulatory modules. We apply this present method to ovarian cancer datasets and make a comparison with the other two existing prominent approaches. The results indicate that the modules identified by CoModule perform better than the other two methods ranging from the topological aspects to the biological function. Survival analysis detects a number of prognostic modules with statistical significance, which can help reveal the potential diagnostic for ovarian cancer.

Predicting MiRNA-Disease Association by Latent Feature Extraction with Positive Samples

In discovering disease etiology and pathogenesis, the associations between MicroRNAs (miRNAs) and diseases play a critical role. Given known miRNA-disease associations (MDAs), how to uncover potential MDAs is an important problem. To solve this problem, most of the existing methods regard known MDAs as positive samples and unknown ones as negative samples, and then predict possible MDAs by iteratively revising the negative samples. However, simply viewing unknown MDAs as negative samples introduces erroneous information, which may result in poor predication performance. To avoid such defects, we present a novel method using only positive samples to predict MDAs by latent features extraction (LFEMDA). We design a new approach to construct the miRNAs similarity matrix. LFEMDA integrates the disease similarity matrix, the known MDAs and the miRNAs similarity matrix to identify potential MDAs. By introducing miRNAs and diseases knowledge as the auxiliary variables, the method can converge to give the optimal solution in each iteration. We conduct experiments on high-association diseases and new diseases datasets, in which our method shows better performance than that of other methods. We also carry out a case study on breast neoplasms to further demonstrate the capacity of our method in uncovering potential MDAs.

Revealing the Presence of a Symbolic Sequence Representing Multiple Nucleotides Based on K-Means Clustering of Oligonucleotides

In biological systems, a few sequence differences diversify the hybridization profile of nucleotides and enable the quantitative control of cellular metabolism in a cooperative manner. In this respect, the information required for a better understanding may not be in each nucleotide sequence, but representative information contained among them. Existing methodologies for nucleotide sequence design have been optimized to track the function of the genetic molecule and predict interaction with others. However, there has been no attempt to extract new sequence information to represent their inheritance function. Here, we tried to conceptually reveal the presence of a representative sequence from groups of nucleotides. The combined application of the K-means clustering algorithm and the social network analysis theorem enabled the effective calculation of the representative sequence. First, a “common sequence” is made that has the highest hybridization property to analog sequences. Next, the sequence complementary to the common sequence is designated as a ‘representative sequence’. Based on this, we obtained a representative sequence from multiple analog sequences that are 8–10-bases long. Their hybridization was empirically tested, which confirmed that the common sequence had the highest hybridization tendency, and the representative sequence better alignment with the analogs compared to a mere complementary.

Inferring MicroRNA Targets Based on Restricted Boltzmann Machines

Predicting the miRNA-target interactions (MTIs) is a critical task for elucidating mechanistic roles of miRNAs in pathophysiology. However, most existing techniques have a higher false positive because the precise miRNA target mechanisms are poorly known. Considering that ensemble methods can take advantage of the complementary knowledge in different methods, we propose an alternative optimization framework, Inferring MiRNA Targets based on Restricted Boltzmann Machines (IMTRBM), to enhance the accuracy of previous prediction results. First, the proposed method directly constructs a weighted MTI network though the results predicted by individual methods and each miRNA target pair is weighted based on the frequency appearing in these results. Second, we transform the miRNA-target prediction problem into a complete bipartite graph model, named restricted Boltzmann machine, and utilize a practical learning procedure to train our model and make predictions. Our results show that the algorithm outperforms individual miRNA-target prediction approach in the number of validated miRNA targets at cutoffs of top list. Moreover, our framework can tolerate the decrease and increase of predicted MTIs and even discover new miRNA targets, which have been a challenge to predict for any individual methods. Finally, for the miRNAs that are not appearing in IMTRBM, we design a new method to supplement IMTRBM based on the intuition that similar miRNAs have similar functions, which also achieves a comparable result. The source code of IMTRBM is available at https://github.com/liuying201705/IMTRBM.

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.

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 AND IMPLEMENTATION

The code of GRNMF is freely available at https://github.com/XIAO-HN/GRNMF/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.