Disease-related miRNAs Research Articles

Predicting miRNA-Disease Associations by Incorporating Projections in Low-Dimensional Space and Local Topological Information.

Predicting the potential microRNA (miRNA) candidates associated with a disease helps in exploring the mechanisms of disease development. Most recent approaches have utilized heterogeneous information about miRNAs and diseases, including miRNA similarities, disease similarities, and miRNA-disease associations. However, these methods do not utilize the projections of miRNAs and diseases in a low-dimensional space. Thus, it is necessary to develop a method that can utilize the effective information in the low-dimensional space to predict potential disease-related miRNA candidates. We proposed a method based on non-negative matrix factorization, named DMAPred, to predict potential miRNA-disease associations. DMAPred exploits the similarities and associations of diseases and miRNAs, and it integrates local topological information of the miRNA network. The likelihood that a miRNA is associated with a disease also depends on their projections in low-dimensional space. Therefore, we project miRNAs and diseases into low-dimensional feature space to yield their low-dimensional and dense feature representations. Moreover, the sparse characteristic of miRNA-disease associations was introduced to make our predictive model more credible. DMAPred achieved superior performance for 15 well-characterized diseases with AUCs (area under the receiver operating characteristic curve) ranging from 0.860 to 0.973 and AUPRs (area under the precision-recall curve) ranging from 0.118 to 0.761. In addition, case studies on breast, prostatic, and lung neoplasms demonstrated the ability of DMAPred to discover potential disease-related miRNAs.

Crosstalk between microRNAs, the putative target genes and the lncRNA network in metabolic diseases.

MicroRNAs (miRNAs/miRs) are small non-coding RNAs (ncRNAs) that regulate gene expression. Emerging knowledge has suggested that miRNAs have a role in the pathogenesis of metabolic disorders, supporting the hypothesis that miRNAs may represent potential biomarkers or targets for this set of diseases. However, the current evidence is often controversial. Therefore, the aim of the present study was to determine the associations between miRNAs-target genes, miRNA-long ncRNAs (lncRNAs), and miRNAs-small molecules in human metabolic diseases, including obesity, type 2 diabetes and non-alcoholic fatty liver disease. The metabolic disease-related miRNAs were obtained from the Human MicroRNA Disease Database (HMDD) and miR2Disease database. A search on the databases Matrix Decomposition and Heterogeneous Graph Inference (MDHGI) and DisGeNET were also performed. miRNAs target genes were obtained from three independent sources: Microcosm, TargetScan and miRTarBase. The interactions between miRNAs-lncRNA and miRNA-small molecules were performed using the miRNet web tool. The network analyses were performed using Cytoscape software. As a result, a total of 20 miRNAs were revealed to be associated with metabolic disorders in the present study. Notably, 6 miRNAs (miR-17-5p, miR-29c-3p, miR-34a-5p, miR-103a-3p, miR-107 and miR-132-3p) were found in the four resources (HMDD, miR2Disease, MDHGI, and DisGeNET) used for these analyses, presenting a stronger association with the diseases. Furthermore, the target genes of these miRNAs participate in several pathways previously associated with metabolic diseases. In addition, interactions between miRNA-lncRNA and miRNA-small molecules were also found, suggesting that some molecules can modulate gene expression via such an indirect way. Thus, the results of this data mining and integration analysis provide further information on the possible molecular basis of the metabolic disease pathogenesis as well as provide a path to search for potential biomarkers and therapeutic targets concerning metabolic diseases.

A Fast Linear Neighborhood Similarity-Based Network Link Inference Method to Predict MicroRNA-Disease Associations.

Increasing evidences revealed that microRNAs (miRNAs) play critical roles in important biological processes. The identification of disease-related miRNAs is critical to understand the molecular mechanisms of human diseases. Most existing computational methods require diverse features to predict miRNA-disease associations. However, diverse features are not available for all miRNAs or diseases. In addition, most methods can't predict links for miRNAs or diseases without association information. In this paper, we propose a fast linear neighborhood similarity-based network link inference method, named FLNSNLI, to predict miRNA-disease associations. First, known miRNA-disease associations are formulated as a bipartite network, and miRNAs (or diseases) are expressed as association profiles. Second, miRNA-miRNA similarity and disease-disease similarity are calculated by fast linear neighborhood similarity measure and association profiles. Third, the label propagation algorithm is respectively implemented on two sides to score candidate miRNA-disease associations. Finally, FLNSNLI adopts the weighted average strategy and makes predictions. Moreover, we develop a link complementing approach, and extend FLNSNLI to predict links for miRNAs (or diseases) without known associations. In computational experiments, FLNSNLI produces high-accuracy performances, and outperforms other state-of-the-art methods. More importantly, FLNSNLI requires less information but performs well. Case studies on three popular diseases show that FLNSNLI is useful for the microRNA-disease association prediction.

Artificial Circular RNA Sponges Targeting MicroRNAs as a Novel Tool in Molecular Biology

Artificial Circular RNA Sponges Targeting MicroRNAs as a Novel Tool in Molecular Biology

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.

Adaptive boosting-based computational model for predicting potential miRNA-disease associations.

Motivation Recent studies have shown that microRNAs (miRNAs) play a critical part in several biological processes and dysregulation of miRNAs is related with numerous complex human diseases. Thus, in-depth research of miRNAs and their association with human diseases can help us to solve many problems. Results Due to the high cost of traditional experimental methods, revealing disease-related miRNAs through computational models is a more economical and efficient way. Considering the disadvantages of previous models, in this paper, we developed adaptive boosting for miRNA-disease association prediction (ABMDA) to predict potential associations between diseases and miRNAs. We balanced the positive and negative samples by performing random sampling based on k-means clustering on negative samples, whose process was quick and easy, and our model had higher efficiency and scalability for large datasets than previous methods. As a boosting technology, ABMDA was able to improve the accuracy of given learning algorithm by integrating weak classifiers that could score samples to form a strong classifier based on corresponding weights. Here, we used decision tree as our weak classifier. As a result, the area under the curve (AUC) of global and local leave-one-out cross validation reached 0.9170 and 0.8220, respectively. What is more, the mean and the standard deviation of AUCs achieved 0.9023 and 0.0016, respectively in 5-fold cross validation. Besides, in the case studies of three important human cancers, 49, 50 and 50 out of the top 50 predicted miRNAs for colon neoplasms, hepatocellular carcinoma and breast neoplasms were confirmed by the databases and experimental literatures. Availability and implementation The code and dataset of ABMDA are freely available at https://github.com/githubcode007/ABMDA. Supplementary information Supplementary data are available at Bioinformatics online.

Predicting microRNA‐disease association based on microRNA structural and functional similarity network

BackgroundIncreasing evidences indicate that microRNAs (miRNAs) are functionally related to the development and progression of various human diseases. Inferring disease‐related miRNAs can be helpful in promoting disease biomarker detection for the treatment, diagnosis, and prevention of complex diseases.MethodsTo improve the prediction accuracy of miRNA‐disease association and capture more potential disease‐related miRNAs, we constructed a precise miRNA global similarity network (MSFSN) via calculating the miRNA similarity based on secondary structures, families, and functions.ResultsWe tested the network on the classical algorithms: WBSMDA and RWRMDA through the method of leave‐one‐out cross‐validation. Eventually, AUCs of 0.8212 and 0.9657 are obtained, respectively. Also, the proposed MSFSN is applied to three cancers for breast neoplasms, hepatocellular carcinoma, and prostate neoplasms. Consequently, 82%, 76%, and 82% of the top 50 potential miRNAs for these diseases are respectively validated by the miRNA‐disease associations database miR2Disease and oncomiRDB.ConclusionTherefore, MSFSN provides a novel miRNA similarity network combining precise function network with global structure network of miRNAs to predict the associations between miRNAs and diseases in various models.

Potential miRNA-disease association prediction based on kernelized Bayesian matrix factorization

Many biological experimental studies have confirmed that microRNAs (miRNAs) play a significant role in human complex diseases. Exploring miRNA-disease associations could be conducive to understanding disease pathogenesis at the molecular level and developing disease diagnostic biomarkers. However, since conducting traditional experiments is a costly and time-consuming way, plenty of computational models have been proposed to predict miRNA-disease associations. In this study, we presented a neoteric Bayesian model (KBMFMDA) that combines kernel-based nonlinear dimensionality reduction, matrix factorization and binary classification. The main idea of KBMFMDA is to project miRNAs and diseases into a unified subspace and estimate the association network in that subspace. KBMFMDA obtained the AUCs of 0.9132, 0.8708, 0.9008±0.0044 in global and local leave-one-out and five-fold cross validation. Moreover, KBMFMDA was applied to three important human cancers in three different kinds of case studies and most of the top 50 potential disease-related miRNAs were confirmed by many experimental reports.

Identification of altered microRNAs in retinas of mice with oxygen-induced retinopathy.

To identify disease-related miRNAs in retinas of mice with oxygen-induced retinopathy (OIR), and to explore their potential roles in retinal pathological neovascularization. The retinal miRNA expression profile in mice with OIR and room air controls at postnatal day 17 (P17) were determined through miRNA microarray analysis. Several miRNAs were significantly up- and down-regulated in retinas of mice with OIR compared to controls by quantitative real-time reverse transcription-polymerase chain reaction (qRT-PCR). Two databases including Targetscan7.1 and MirdbV5 were used to predict target genes that associated with those significantly altered miRNAs in retinas of mice with OIR. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were also conducted to identify possible biological functions of the target genes. In comparison with room air controls, 3 and 8 miRNAs were significantly up- and down-regulated, respectively, in retinas of mice with OIR. The qRT-PCR data confirmed that mmu-miR-350-3p and mmu-miR-202-3p were significantly up-regulated, while mmu-miR-711 and mmu-miR-30c-1-3p were significantly down-regulated in mice with OIR compared to controls. GO analysis demonstrated that the identified target genes were related to functions such as cellular macromolecule metabolic process. KEGG pathway analysis showed a group of pathways, such as Wnt signaling pathway, transcriptional misregulation in cancer, Mucin type O-glycan biosynthesis, and mitogen-activated protein kinase (MAPK) signaling pathway might be involved in pathological process of retinal neovascularization. Our findings suggest that the differentially expressed miRNAs in retinas of mice with OIR might provide potential therapeutic targets for treating retinal neovascularization.

A Novel Method for Predicting Disease-Associated LncRNA-MiRNA Pairs Based on the Higher-Order Orthogonal Iteration.

A lot of research studies have shown that many complex human diseases are associated not only with microRNAs (miRNAs) but also with long noncoding RNAs (lncRNAs). However, most of the current existing studies focus on the prediction of disease-related miRNAs or lncRNAs, and to our knowledge, until now, there are few literature studies reported to pay attention to the study of impact of miRNA-lncRNA pairs on diseases, although more and more studies have shown that both lncRNAs and miRNAs play important roles in cell proliferation and differentiation during the recent years. The identification of disease-related genes provides great insight into the underlying pathogenesis of diseases at a system level. In this study, a novel model called PADLMHOOI was proposed to predict potential associations between diseases and lncRNA-miRNA pairs based on the higher-order orthogonal iteration, and in order to evaluate its prediction performance, the global and local LOOCV were implemented, respectively, and simulation results demonstrated that PADLMHOOI could achieve reliable AUCs of 0.9545 and 0.8874 in global and local LOOCV separately. Moreover, case studies further demonstrated the effectiveness of PADLMHOOI to infer unknown disease-related lncRNA-miRNA pairs.

Adaptive boosting-based computational model for predicting potential miRNA-disease associations.

Recent studies have shown that microRNAs (miRNAs) play a critical part in several biological processes and dysregulation of miRNAs is related with numerous complex human diseases. Thus, in-depth research of miRNAs and their association with human diseases can help us to solve many problems. Due to the high cost of traditional experimental methods, revealing disease-related miRNAs through computational models is a more economical and efficient way. Considering the disadvantages of previous models, in this paper, we developed adaptive boosting for miRNA-disease association prediction (ABMDA) to predict potential associations between diseases and miRNAs. We balanced the positive and negative samples by performing random sampling based on k-means clustering on negative samples, whose process was quick and easy, and our model had higher efficiency and scalability for large datasets than previous methods. As a boosting technology, ABMDA was able to improve the accuracy of given learning algorithm by integrating weak classifiers that could score samples to form a strong classifier based on corresponding weights. Here, we used decision tree as our weak classifier. As a result, the area under the curve (AUC) of global and local leave-one-out cross validation reached 0.9170 and 0.8220, respectively. What is more, the mean and the standard deviation of AUCs achieved 0.9023 and 0.0016, respectively in 5-fold cross validation. Besides, in the case studies of three important human cancers, 49, 50 and 50 out of the top 50 predicted miRNAs for colon neoplasms, hepatocellular carcinoma and breast neoplasms were confirmed by the databases and experimental literatures. The code and dataset of ABMDA are freely available at https://github.com/githubcode007/ABMDA. Supplementary data are available at Bioinformatics online.

FCMDAP: using miRNA family and cluster information to improve the prediction accuracy of disease related miRNAs

BackgroundBiological experiments have confirmed the association between miRNAs and various diseases. However, such experiments are costly and time consuming. Computational methods help select potential disease-related miRNAs to improve the efficiency of biological experiments.MethodsIn this work, we develop a novel method using multiple types of data to calculate miRNA and disease similarity based on mutual information, and add miRNA family and cluster information to predict human disease-related miRNAs (FCMDAP). This method not only depends on known miRNA-diseases associations but also accurately measures miRNA and disease similarity and resolves the problem of overestimation. FCMDAP uses the k most similar neighbor recommendation algorithm to predict the association score between miRNA and disease. Information about miRNA cluster is also used to improve prediction accuracy.ResultFCMDAP achieves an average AUC of 0.9165 based on leave-one-out cross validation. Results confirm the 100, 98 and 96% of the top 50 predicted miRNAs reported in case studies on colorectal, lung, and pancreatic neoplasms. FCMDAP also exhibits satisfactory performance in predicting diseases without any related miRNAs and miRNAs without any related diseases.ConclusionsIn this study, we present a computational method FCMDAP to improve the prediction accuracy of disease related miRNAs. FCMDAP could be an effective tool for further biological experiments.

Expression of disease-related miRNAs in white-matter lesions of progressive multiple sclerosis brains.

BackgroundMicroRNA (miRNA) expression in the serum of multiple sclerosis (MS) patients has been correlated with white matter (WM) magnetic resonance imaging (MRI) abnormalities. The expression levels and cellular specificity of the target genes of these miRNAs are unknown in MS brain.ObjectiveThe aim of this study was to analyze and validate the expression of miRNAs, previously reported as dysregulated in sera of MS patients, in white‐matter lesions (WMLs) of progressive MS brains.MethodsWe performed global miRNA expression profiling analysis in demyelinated WMLs of progressive MS brains (n = 5) and compared the significantly altered miRNAs to previously identified miRNAs from sera of MS patients. Top dysregulated miRNAs common between the two datasets were validated in an independent cohort of MS brains by quantitative PCR (qPCR) and in situ hybridization.ResultsAmong the miRNAs that were significantly changed in WML tissues, 11 were similar to pathogenic and 12 were common to protective miRNAs previously identified in sera and correlating with WM MRI abnormalities. Importantly, the expression levels of 58% of the protective miRNAs (7 of 12) were decreased in MS lesions compared to surrounding normal‐appearing tissue. Target genes of these miRNAs were also altered in MS lesions and queries of cell‐specific databases identified astrocytes and microglia as the key cellular expressers of these genes in MS brains.ConclusionsWe identified miRNAs that correlate with MRI abnormalities in lesioned tissue from MS brains.

In silico drug repositioning based on drug-miRNA associations.

Drug repositioning has become a prevailing tactic as this strategy is efficient, economical and low risk for drug discovery. Meanwhile, recent studies have confirmed that small-molecule drugs can modulate the expression of disease-related miRNAs, which indicates that miRNAs are promising therapeutic targets for complex diseases. In this study, we put forward and verified the hypothesis that drugs with similar miRNA profiles may share similar therapeutic properties. Furthermore, a comprehensive drug-drug interaction network was constructed based on curated drug-miRNA associations. Through random network comparison, topological structure analysis and network module extraction, we found that the closely linked drugs in the network tend to treat the same diseases. Additionally, the curated drug-disease relationships (from the CTD) and random walk with restarts algorithm were utilized on the drug-drug interaction network to identify the potential drugs for a given disease. Both internal validation (leave-one-out cross-validation) and external validation (independent drug-disease data set from the ChEMBL) demonstrated the effectiveness of the proposed approach. Finally, by integrating drug-miRNA and miRNA-disease information, we also explain the modes of action of drugs in the view of miRNA regulation. In summary, our work could determine novel and credible drug indications and offer novel insights and valuable perspectives for drug repositioning.

Integrating random walk and binary regression to identify novel miRNA-disease association

BackgroundIn the last few decades, cumulative experimental researches have witnessed and verified the important roles of microRNAs (miRNAs) in the development of human complex diseases. Benefitting from the rapid growth both in the availability of miRNA-related data and the development of various analysis methodologies, up until recently, some computational models have been developed to predict human disease related miRNAs, efficiently and quickly.ResultsIn this work, we proposed a computational model of Random Walk and Binary Regression-based MiRNA-Disease Association prediction (RWBRMDA). RWBRMDA extracted features for each miRNA from random walk with restart on the integrated miRNA similarity network for binary logistic regression to predict potential miRNA-disease associations. RWBRMDA obtained AUC of 0.8076 in the leave-one-out cross validation. Additionally, we carried out three different patterns of case studies on four human complex diseases. Specifically, Esophageal cancer and Prostate cancer were conducted as one kind of case study based on known miRNA-disease associations in HMDD v2.0 database. Out of the top 50 predicted miRNAs, 94 and 90% were respectively confirmed by recent experimental reports. To simulate new disease without known related miRNAs, the information of known Breast cancer related miRNAs was removed. As a result, 98% of the top 50 predicted miRNAs for Breast cancer were confirmed. Lymphoma, the verified ratio of which was 88%, was used to assess the prediction robustness of RWBRMDA based on the association records in HMDD v1.0 database.ConclusionsWe anticipated that RWBRMDA could benefit the future experimental investigations about the relation between human disease and miRNAs by generating promising and testable top-ranked miRNAs, and significantly reducing the effort and cost of identification works.

Integrated In-Silico analysis of long non-coding RNAs association with sheep diseases

Long non-coding RNAs (lncRNAs) are mRNA like transcripts longer than 200 nucleotides. They play regulatory roles in several biological processes such as transcriptional regulation, epigenetic modification, development, regulation of immune response. Studies have also shown that many complex human diseases are associated with lncRNAs, database such as lncRNA disease has explored human long non-coding RNAs association with diseases. However, there is very few literature studies reported to pay attention to the study of impact of lncRNAs on livestock diseases. Public repository such as lncRNAdb and ALDB, has only described the status of long non-coding RNAs in livestock species. However, none of these databases focused on sheep lncRNAs information and their association with diseases. In the present study, we proposed that lncRNAs transcripts, derived from sheep genome can be used as a marker for the identification of various disease. The lncRNA transcripts were found closer to sheep disease related genes such as CLCN1 and FGFR3. Several disease-associated microRNAs (miRNAs) such as miR-26b, miR-21 and miR-544 interaction with lncRNA transcripts were also identified. The current research will provide a functional annotation of the sheep genome. The association of lncRNAs with disease-related genes and miRNAs, provides great insight into the underlying pathogenesis of diseases.

CGMDA: An Approach to Predict and Validate MicroRNA-Disease Associations by Utilizing Chaos Game Representation and LightGBM

Recent studies have shown that microRNAs (miRNAs) play an important role in complex human diseases. Identifying potential miRNA-disease associations is useful for understanding the pathogenesis. However, there are currently only a few methods proposed to predict miRNA-disease association based on sequence information. And these methods can only quantify nonlinear sequence relationships without taking linear sequence information into account. In this work, we designed a computational method for predicting miRNA-disease association based on chaos game representation, called CGMDA, to overcome these problems. CGMDA combines association information with miRNA sequence information, miRNA functional information and disease semantic information to improve prediction accuracy. In particular, we use chaos game representation (CGR) technology for the first time to transform miRNA sequence information into image information and extract its features. In the cross-validation experiment, CGMDA achieved a mean the area under the receiver operating characteristic curve (AUC) of 0.9099 on the HMDD v3.0 data set. To better evaluate the performance of CGMDA, we compared it to different classifiers and related prediction methods. In addition, CGMDA is applied to three human complex diseases. The results showed that of the top 40 disease-related miRNAs predicted, 39 (Breast Neoplasm), 39 (Lymphoma) and 38 (Colon Neoplasm) were validated by experiments in case studies. These experimental results show that CGMDA is a reliable tool and has potential application prospects in assisting early diagnosis and treatment of prognosis.

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.

A pilot study of blood microRNAs and lung function in young healthy adults with fine particulate matter exposure

Fine particulate exposure (PM2.5) is a risk factor of pulmonary diseases such as chronic obstructive pulmonary disease (COPD), but the mechanism underlying was not clear. Recent studies found blood microRNAs (miRNAs) are potential indicators of either COPD or PM2.5 exposure, but these results had no unified conclusions. We suggested it was more targeted to find disease related miRNAs first and then observe them during PM2.5 exposure. Firstly, in order to screen COPD associated miRNAs, we identified differentially expressed blood miRNAs contrasting COPD participants (n=6) without diagnose of COPD or related treatment before and matched control (n=6). In total, 21 miRNAs were differentially expressed in COPD individuals and expression of miR-495-3p, miR-223-5p and miR-194-3p were further validated using qRT-PCR. The results showed miR-495-3p and miR-223-5p significantly increased whereas miR-194-3p decreased marginally (P=0.058) in COPD participants. Secondly, in order to recognize the relevance between these miRNAs and PM2.5 exposure, we designed an independent time-series study nested within "low-high-low" pollution levels. The expression of blood miR-495-3p, miR-223-5p and miR-194-3p were detected before and after exposure (n=8). The results showed expression of miR-223-5p increased significantly while expression of miR-194-3p decreased significantly after exposure. The Pearson analysis showed only miR-194-3p showed a positive statistically correlation with lag0-1 forced expiratory volume in one second (FEV1) and forced vital capacity (FVC) during exposure of PM2.5. So miR-194-3p might be a potential regulator in the toxicological pathways of both PM2.5 exposure and COPD. As this was a pilot study, formal and large-scale studies should be planned in the future.

Dual Convolutional Neural Network Based Method for Predicting Disease-Related miRNAs.

Identification of disease-related microRNAs (disease miRNAs) is helpful for understanding and exploring the etiology and pathogenesis of diseases. Most of recent methods predict disease miRNAs by integrating the similarities and associations of miRNAs and diseases. However, these methods fail to learn the deep features of the miRNA similarities, the disease similarities, and the miRNA–disease associations. We propose a dual convolutional neural network-based method for predicting candidate disease miRNAs and refer to it as CNNDMP. CNNDMP not only exploits the similarities and associations of miRNAs and diseases, but also captures the topology structures of the miRNA and disease networks. An embedding layer is constructed by combining the biological premises about the miRNA–disease associations. A new framework based on the dual convolutional neural network is presented for extracting the deep feature representation of associations. The left part of the framework focuses on integrating the original similarities and associations of miRNAs and diseases. The novel miRNA and disease similarities which contain the topology structures are obtained by random walks on the miRNA and disease networks, and their deep features are learned by the right part of the framework. CNNDMP achieves the superior prediction performance than several state-of-the-art methods during the cross-validation process. Case studies on breast cancer, colorectal cancer and lung cancer further demonstrate CNNDMP’s powerful ability of discovering potential disease miRNAs.