Prediction of disease-related interactions between microRNAs and environmental factors based on a semi-supervised classifier

Bioentity2vec: Attribute- and behavior-driven representation for predicting multi-type relationships between bioentities

BACKGROUND

The explosive growth of genomic, chemical, and pathological data provides new opportunities and challenges for humans to thoroughly understand life activities in cells. However, there exist few computational models that aggregate various bioentities to comprehensively reveal the physical and functional landscape of biological systems.

RESULTS

We constructed a molecular association network, which contains 18 edges (relationships) between 8 nodes (bioentities). Based on this, we propose Bioentity2vec, a new method for representing bioentities, which integrates information about the attributes and behaviors of a bioentity. Applying the random forest classifier, we achieved promising performance on 18 relationships, with an area under the curve of 0.9608 and an area under the precision-recall curve of 0.9572.

CONCLUSIONS

Our study shows that constructing a network with rich topological and biological information is important for systematic understanding of the biological landscape at the molecular level. Our results show that Bioentity2vec can effectively represent biological entities and provides easily distinguishable information about classification tasks. Our method is also able to simultaneously predict relationships between single types and multiple types, which will accelerate progress in biological experimental research and industrial product development.

GBDTL2E: Predicting lncRNA-EF Associations Using Diffusion and HeteSim Features Based on a Heterogeneous Network

Interactions between genetic factors and environmental factors (EFs) play an important role in many diseases. Many diseases result from the interaction between genetics and EFs. The long non-coding RNA (lncRNA) is an important non-coding RNA that regulates life processes. The ability to predict the associations between lncRNAs and EFs is of important practical significance. However, the recent methods for predicting lncRNA-EF associations rarely use the topological information of heterogenous biological networks or simply treat all objects as the same type without considering the different and subtle semantic meanings of various paths in the heterogeneous network. In order to address this issue, a method based on the Gradient Boosting Decision Tree (GBDT) to predict the association between lncRNAs and EFs (GBDTL2E) is proposed in this paper. The innovation of the GBDTL2E integrates the structural information and heterogenous networks, combines the Hetesim features and the diffusion features based on multi-feature fusion, and uses the machine learning algorithm GBDT to predict the association between lncRNAs and EFs based on heterogeneous networks. The experimental results demonstrate that the proposed algorithm achieves a high performance.

SNMFSMMA: using symmetric nonnegative matrix factorization and Kronecker regularized least squares to predict potential small molecule-microRNA association

Accumulating studies have shown that microRNAs (miRNAs) could be used as targets of small-molecule (SM) drugs to treat diseases. In recent years, researchers have proposed many computational models to reveal miRNA-SM associations due to the huge cost of experimental methods. Considering the shortcomings of the previous models, such as the prediction accuracy of some models is low or some cannot be applied for new SMs (miRNAs), we developed a novel model named Symmetric Nonnegative Matrix Factorization for Small Molecule-MiRNA Association prediction (SNMFSMMA). Different from some models directly applying the integrated similarities, SNMFSMMA first performed matrix decomposition on the integrated similarity matrixes, and calculated the Kronecker product of the new integrated similarity matrixes to obtain the SM-miRNA pair similarity. Further, we applied regularized least square to obtain the mapping function of the SM-miRNA pairs to the associated probabilities by minimizing the objective function. On the basis of Dataset 1 and 2 extracted from SM2miR v1.0 database, we implemented global leave-one-out cross validation (LOOCV), miRNA-fixed local LOOCV, SM-fixed local LOOCV and 5-fold cross-validation to evaluate the prediction performance. Finally, the AUC values obtained by SNMFSMMA in these validation reached 0.9711 (0.8895), 0.9698 (0.8884), 0.8329 (0.7651) and 0.9644 ± 0.0035 (0.8814 ± 0.0033) based on Dataset 1 (Dataset 2), respectively. In the first case study, 5 of the top 10 associations predicted were confirmed. In the second, 7 and 8 of the top 10 predicted miRNAs related with 5-FU and 5-Aza-2'-deoxycytidine were confirmed. These results demonstrated the reliable predictive power of SNMFSMMA.

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.

Inferring microRNA-Environmental Factor Interactions Based on Multiple Biological Information Fusion

Accumulated studies have shown that environmental factors (EFs) can regulate the expression of microRNA (miRNA) which is closely associated with several diseases. Therefore, identifying miRNA-EF associations can facilitate the study of diseases. Recently, several computational methods have been proposed to explore miRNA-EF interactions. In this paper, a novel computational method, MEI-BRWMLL, is proposed to uncover the relationship between miRNA and EF. The similarities of miRNA-miRNA are calculated by using miRNA sequence, miRNA-EF interaction, and the similarities of EF-EF are calculated based on the anatomical therapeutic chemical information, chemical structure and miRNA-EF interaction. The similarity network fusion is used to fuse the similarity between miRNA and the similarity between EF, respectively. Further, the multiple-label learning and bi-random walk are employed to identify the association between miRNA and EF. The experimental results show that our method outperforms the state-of-the-art algorithms.

Predicting microRNA-disease associations using bipartite local models and hubness-aware regression

The development and progression of numerous complex human diseases have been confirmed to be associated with microRNAs (miRNAs) by various experimental and clinical studies. Predicting potential miRNA-disease associations can help us understand the underlying molecular and cellular mechanisms of diseases and promote the development of disease treatment and diagnosis. Due to the high cost of conventional experimental verification, proposing a new computational method for miRNA-disease association prediction is an efficient and economical way. Since previous computational models ignored the hubness phenomenon, we presented a novel computational model of Bipartite Local models and Hubness-Aware Regression for MiRNA-Disease Association prediction (BLHARMDA). In this method, we first used known miRNA-disease associations to calculate the Jaccard similarity between miRNAs and between diseases, then utilized a modified kNNs model in the bipartite local model method. As a result, we effectively alleviated the detriments from 'bad' hubs. BLHARMDA obtained AUCs of 0.9141 and 0.8390 in the global and local leave-one-out cross validation, respectively, which outperformed most of the previous models and proved high prediction performance of BLHARMDA. Besides, the standard deviation of 0.0006 in 5-fold cross validation confirmed our model's prediction stability and the averaged prediction accuracy of 0.9120 showed the high precision of our model. In addition, to further evaluate our model's accuracy, we implemented BLHARMDA on three typical human diseases in three different types of case studies. As a result, 49 (Esophageal Neoplasms), 50 (Lung Neoplasms) and 50 (Carcinoma Hepatocellular) out of the top 50 related miRNAs were validated by recent experimental discoveries.

Identifying and Exploiting Potential miRNA-Disease Associations With Neighborhood Regularized Logistic Matrix Factorization

With the rapid development of biological research, microRNAs (miRNA) have become an attractive topic because lots of experimental studies have revealed the significant associations between miRNAs and diseases. However, considering that experiments are expensive and time-consuming, computational methods for predicting associations between miRNAs and diseases have become increasingly crucial. In this study, we proposed a neighborhood regularized logistic matrix factorization method for miRNA-disease association prediction (NRLMFMDA) by integrating miRNA functional similarity, disease semantic similarity, Gaussian interaction profile kernel similarity, and experimentally validation of disease-miRNA association. We used Gaussian interaction profile kernel similarity to cover the shortage of the traditional similarity to make it more reasonable and complete. Furthermore, NRLMFMDA also considered the important influences of the neighborhood information and took full advantage of them to improve the accuracy of the miRNA-disease association prediction. We also improved the accuracy by giving higher weights to the known association data in the process of calculating the potential association probabilities. In the global and the local leave-one-out cross validation, NRLMFMDA got the AUCs of 0.9068 and 0.8239, respectively. Moreover, the average AUC of NRLMFMDA in 5-fold cross validation was 0.8976 ± 0.0034. All the three kinds of cross validations have shown significant advantages to a number of previous models. In the case studies of breast neoplasms, esophageal neoplasms and lymphoma according to known miRNA-disease associations in the recent version of HMDD database, there were 78, 80, and 74% of top 50 predicted related miRNAs verified to have associations with these three diseases, respectively. In the further case studies for new disease without any known related miRNAs and the previous version of HMDD database, there were also high proportions of the predicted miRNAs verified by experimental reports. All the validation experiment results have demonstrated the effectiveness and practicability of NRLFMDA to predict the potential miRNA-disease associations.

TLHNMDA: Triple Layer Heterogeneous Network Based Inference for MiRNA-Disease Association Prediction

In recent years, microRNAs (miRNAs) have been confirmed to be involved in many important biological processes and associated with various kinds of human complex diseases. Therefore, predicting potential associations between miRNAs and diseases with the huge number of verified heterogeneous biological datasets will provide a new perspective for disease therapy. In this article, we developed a novel computational model of Triple Layer Heterogeneous Network based inference for MiRNA-Disease Association prediction (TLHNMDA) by using the experimentally verified miRNA-disease associations, miRNA-long noncoding RNA (lncRNA) interactions, miRNA function similarity information, disease semantic similarity information and Gaussian interaction profile kernel similarity for lncRNAs into an triple layer heterogeneous network to predict new miRNA-disease associations. As a result, the AUCs of TLHNMDA are 0.8795 and 0.8795 ± 0.0010 based on leave-one-out cross validation (LOOCV) and 5-fold cross validation, respectively. Furthermore, TLHNMDA was implemented on three complex human diseases to evaluate predictive ability. As a result, 84% (kidney neoplasms), 78% (lymphoma) and 76% (prostate neoplasms) of top 50 predicted miRNAs for the three complex diseases can be verified by biological experiments. In addition, based on the HMDD v1.0 database, 98% of top 50 potential esophageal neoplasms-associated miRNAs were confirmed by experimental reports. It is expected that TLHNMDA could be a useful model to predict potential miRNA-disease associations with high prediction accuracy and stability.

Semi-Supervised Maximum Discriminative Local Margin for Gene Selection

In the present study, we introduce a novel semi-supervised method called the semi-supervised maximum discriminative local margin (semiMM) for gene selection in expression data. The semiMM is a "filter" approach that exploits local structure, variance, and mutual information. We first constructed a local nearest neighbour graph and divided this information into within-class and between-class local nearest neighbour graphs by weighing the edge between the two data points. The semiMM aims to discover the most discriminative features for classification via maximizing the local margin between the within-class and between-class data, the variance of all data, and the mutual information of features with class labels. Experiments on five publicly available gene expression datasets revealed the effectiveness of the proposed method compared to three state-of-the-art feature selection algorithms.

ELLPMDA: Ensemble learning and link prediction for miRNA-disease association prediction

Recently, accumulating evidences have indicated miRNAs play critical roles in the progression and development of various human complex diseases, which pointed out that identifying miRNA-disease association could enable us to understand diseases at miRNA level. Thus, revealing more and more potential miRNA-disease associations is a vital topic in biomedical domain. However, it will be extremely expensive and time-consuming if we examine all the possible miRNA-disease pairs. Therefore, more accurate and efficient methods are being highly requested to detect potential miRNA-disease associations. In this study, we developed a computational model of Ensemble Learning and Link Prediction for miRNA-Disease Association prediction (ELLPMDA) to achieve this goal. By integrating miRNA functional similarity, disease semantic similarity, miRNA-disease association and Gaussian profile kernel similarity for miRNAs and diseases, we constructed a similarity network and utilized ensemble learning to combine rank results given by three classic similarity-based algorithms. To evaluate the performance of ELLPMDA, we exploited global and local Leave-One-Out Cross Validation (LOOCV), 5-fold Cross Validation (CV) and three kinds of case studies. As a result, the AUCs of ELLPMDA is 0.9181, 0.8181 and 0.9193+/-0.0002 in global LOOCV, local LOOCV and 5-fold CV, respectively, which significantly exceed almost all the previous methods. Moreover, in three distinct kinds of case studies for Kidney Neoplasms, Lymphoma, Prostate Neoplasms, Colon Neoplasms and Esophageal Neoplasms, 88%, 92%, 86%, 98% and 98% out of the top 50 predicted miRNAs has been confirmed, respectively. Besides, ELLPMDA is based on global similarity measure and applicable to new diseases without any known related miRNAs.

A Bipartite Network and Resource Transfer-Based Approach to Infer lncRNA-Environmental Factor Associations

Phenotypes and diseases are often determined by the complex interactions between genetic factors and environmental factors (EFs). However, compared with protein-coding genes and microRNAs, there is a paucity of computational methods for understanding the associations between long non-coding RNAs (lncRNAs) and EFs. In this study, we focused on the associations between lncRNA and EFs. By using the common miRNA partners of any pair of lncRNA and EF, based on the competing endogenous RNA (ceRNA) hypothesis and the technique of resources transfer within the experimentally-supported lncRNA-miRNA and miRNA-EF association bipartite networks, we propose an algorithm for predicting new lncRNA-EF associations. Results show that, compared with another recently-proposed method, our approach is capable of predicting more credible lncRNA-EF associations. These results support the validity of our approach to predict biologically significant associations, which could lead to a better understanding of the molecular processes.

A survey on database resources for microRNA-disease relationships

The relationships between microRNAs (miRNAs) and diseases are garnering greater interest in the biological research fields. Recently, miRNA-disease databases have emerged as powerful tools for bioinformatics studies of these relationships. However, guidelines for comparing the features of this type of database have not yet been established. In this article, the details of popular miRNA-disease databases are analyzed, and their features are compared from several different aspects, including database scale, disease classification, miRNA targets, miRNA detection technique, miRNA regulation, quantitative scores, study design and tissue/cell lines. Then, guidelines for choosing a suitable database for specific research interests are provided. This survey will guide computational biology or biological researchers as well as medical and clinical researchers in making better use of miRNA-disease data resources.

Identification of associations between small molecule drugs and miRNAs based on functional similarity

MicroRNAs (miRNAs) are a class of small non-coding RNA molecules that regulate gene expression at post-transcriptional level. Increasing evidences show aberrant expression of miRNAs in varieties of diseases. Targeting the dysregulated miRNAs with small molecule drugs has become a novel therapy for many human diseases, especially cancer. Here, we proposed a novel computational approach to identify associations between small molecules and miRNAs based on functional similarity of differentially expressed genes. At the significance level of p < 0.01, we constructed the small molecule and miRNA functional similarity network involving 111 small molecules and 20 miRNAs. Moreover, we also predicted associations between drugs and diseases through integrating our identified small molecule-miRNA associations with experimentally validated disease related miRNAs. As a result, we identified 2265 associations between FDA approved drugs and diseases, in which ~35% associations have been validated by comprehensive literature reviews. For breast cancer, we identified 19 potential drugs, in which 12 drugs were supported by previous studies. In addition, we performed survival analysis for the patients from TCGA and GEO database, which indicated that the associated miRNAs of 4 drugs might be good prognosis markers in breast cancer. Collectively, this study proposed a novel approach to predict small molecule and miRNA associations based on functional similarity, which may pave a new way for miRNA-targeted therapy and drug repositioning.

HGIMDA: Heterogeneous graph inference for miRNA-disease association prediction

Recently, microRNAs (miRNAs) have drawn more and more attentions because accumulating experimental studies have indicated miRNA could play critical roles in multiple biological processes as well as the development and progression of human complex diseases. Using the huge number of known heterogeneous biological datasets to predict potential associations between miRNAs and diseases is an important topic in the field of biology, medicine, and bioinformatics. In this study, considering the limitations in the previous computational methods, we developed the computational model of Heterogeneous Graph Inference for MiRNA-Disease Association prediction (HGIMDA) to uncover potential miRNA-disease associations by integrating miRNA functional similarity, disease semantic similarity, Gaussian interaction profile kernel similarity, and experimentally verified miRNA-disease associations into a heterogeneous graph. HGIMDA obtained AUCs of 0.8781 and 0.8077 based on global and local leave-one-out cross validation, respectively. Furthermore, HGIMDA was applied to three important human cancers for performance evaluation. As a result, 90% (Colon Neoplasms), 88% (Esophageal Neoplasms) and 88% (Kidney Neoplasms) of top 50 predicted miRNAs are confirmed by recent experiment reports. Furthermore, HGIMDA could be effectively applied to new diseases and new miRNAs without any known associations, which overcome the important limitations of many previous computational models.

IRWRLDA: improved random walk with restart for lncRNA-disease association prediction

In recent years, accumulating evidences have shown that the dysregulations of lncRNAs are associated with a wide range of human diseases. It is necessary and feasible to analyze known lncRNA-disease associations, predict potential lncRNA-disease associations, and provide the most possible lncRNA-disease pairs for experimental validation. Considering the limitations of traditional Random Walk with Restart (RWR), the model of Improved Random Walk with Restart for LncRNA-Disease Association prediction (IRWRLDA) was developed to predict novel lncRNA-disease associations by integrating known lncRNA-disease associations, disease semantic similarity, and various lncRNA similarity measures. The novelty of IRWRLDA lies in the incorporation of lncRNA expression similarity and disease semantic similarity to set the initial probability vector of the RWR. Therefore, IRWRLDA could be applied to diseases without any known related lncRNAs. IRWRLDA significantly improved previous classical models with reliable AUCs of 0.7242 and 0.7872 in two known lncRNA-disease association datasets downloaded from the lncRNADisease database, respectively. Further case studies of colon cancer and leukemia were implemented for IRWRLDA and 60% of lncRNAs in the top 10 prediction lists have been confirmed by recent experimental reports.

GRMDA: Graph Regression for MiRNA-Disease Association Prediction

Nowadays, as more and more associations between microRNAs (miRNAs) and diseases have been discovered, miRNA has gradually become a hot topic in the biological field. Because of the high consumption of time and money on carrying out biological experiments, computational method which can help scientists choose the most likely associations between miRNAs and diseases for further experimental studies is desperately needed. In this study, we proposed a method of Graph Regression for MiRNA-Disease Association prediction (GRMDA) which combines known miRNA-disease associations, miRNA functional similarity, disease semantic similarity, and Gaussian interaction profile kernel similarity. We used Gaussian interaction profile kernel similarity to supplement the shortage of miRNA functional similarity and disease semantic similarity. Furthermore, the graph regression was synchronously performed in three latent spaces, including association space, miRNA similarity space, and disease similarity space, by using two matrix factorization approaches called Singular Value Decomposition and Partial Least-Squares to extract important related attributes and filter the noise. In the leave-one-out cross validation and five-fold cross validation, GRMDA obtained the AUCs of 0.8272 and 0.8080 ± 0.0024, respectively. Thus, its performance is better than some previous models. In the case study of Lymphoma using the recorded miRNA-disease associations in HMDD V2.0 database, 88% of top 50 predicted miRNAs were verified by experimental literatures. In order to test the performance of GRMDA on new diseases with no known related miRNAs, we took Breast Neoplasms as an example by regarding all the known related miRNAs as unknown ones. We found that 100% of top 50 predicted miRNAs were verified. Moreover, 84% of top 50 predicted miRNAs in case study for Esophageal Neoplasms based on HMDD V1.0 were verified to have known associations. In conclusion, GRMDA is an effective and practical method for miRNA-disease association prediction.

GIMDA: Graphlet interaction-based MiRNA-disease association prediction

MicroRNAs (miRNAs) have been confirmed to be closely related to various human complex diseases by many experimental studies. It is necessary and valuable to develop powerful and effective computational models to predict potential associations between miRNAs and diseases. In this work, we presented a prediction model of Graphlet Interaction for MiRNA‐Disease Association prediction (GIMDA) by integrating the disease semantic similarity, miRNA functional similarity, Gaussian interaction profile kernel similarity and the experimentally confirmed miRNA‐disease associations. The related score of a miRNA to a disease was calculated by measuring the graphlet interactions between two miRNAs or two diseases. The novelty of GIMDA lies in that we used graphlet interaction to analyse the complex relationships between two nodes in a graph. The AUCs of GIMDA in global and local leave‐one‐out cross‐validation (LOOCV) turned out to be 0.9006 and 0.8455, respectively. The average result of five‐fold cross‐validation reached to 0.8927 ± 0.0012. In case study for colon neoplasms, kidney neoplasms and prostate neoplasms based on the database of HMDD V2.0, 45, 45, 41 of the top 50 potential miRNAs predicted by GIMDA were validated by dbDEMC and miR2Disease. Additionally, in the case study of new diseases without any known associated miRNAs and the case study of predicting potential miRNA‐disease associations using HMDD V1.0, there were also high percentages of top 50 miRNAs verified by the experimental literatures.

Long non-coding RNAs and complex diseases: from experimental results to computational models

LncRNAs have attracted lots of attentions from researchers worldwide in recent decades. With the rapid advances in both experimental technology and computational prediction algorithm, thousands of lncRNA have been identified in eukaryotic organisms ranging from nematodes to humans in the past few years. More and more research evidences have indicated that lncRNAs are involved in almost the whole life cycle of cells through different mechanisms and play important roles in many critical biological processes. Therefore, it is not surprising that the mutations and dysregulations of lncRNAs would contribute to the development of various human complex diseases. In this review, we first made a brief introduction about the functions of lncRNAs, five important lncRNA-related diseases, five critical disease-related lncRNAs and some important publicly available lncRNA-related databases about sequence, expression, function, etc. Nowadays, only a limited number of lncRNAs have been experimentally reported to be related to human diseases. Therefore, analyzing available lncRNA–disease associations and predicting potential human lncRNA–disease associations have become important tasks of bioinformatics, which would benefit human complex diseases mechanism understanding at lncRNA level, disease biomarker detection and disease diagnosis, treatment, prognosis and prevention. Furthermore, we introduced some state-of-the-art computational models, which could be effectively used to identify disease-related lncRNAs on a large scale and select the most promising disease-related lncRNAs for experimental validation. We also analyzed the limitations of these models and discussed the future directions of developing computational models for lncRNA research.

SSCMDA: spy and super cluster strategy for MiRNA-disease association prediction

In the biological field, the identification of the associations between microRNAs (miRNAs) and diseases has been paid increasing attention as an extremely meaningful study for the clinical medicine. However, it is expensive and time-consuming to confirm miRNA-disease associations by experimental methods. Therefore, in recent years, several effective computational models for predicting the potential miRNA-disease associations have been developed. In this paper, we proposed the Spy and Super Cluster strategy for MiRNA-Disease Association prediction (SSCMDA) based on known miRNA-disease associations, integrated disease similarity and integrated miRNA similarity. For problems of mixed unknown miRNA-disease pairs containing both potential associations and real negative associations, which will lead to inaccurate prediction, spy strategy is adopted by SSCMDA to identify reliable negative samples from the unknown miRNA-disease pairs. Moreover, the super-cluster strategy could gather as many positive samples as possible to improve the accuracy of the prediction by overcoming the shortage of lacking sufficient positive training samples. As a result, the AUCs of global leave-one-out cross validation (LOOCV), local LOOCV and 5-fold cross validation were 0.9007, 0.8747 and 0.8806+/-0.0025, respectively. According to the AUC results, SSCMDA has shown a significant improvement compared with some previous models. We further carried out case studies based on various version of HMDD database to test the prediction performance robustness of SSCMDA. We also implemented case study to examine whether SSCMDA was effective for new diseases without any known associated miRNAs. As a result, a large proportion of the predicted miRNAs have been verified by experimental reports.

A deep ensemble model to predict miRNA-disease association

Cumulative evidence from biological experiments has confirmed that microRNAs (miRNAs) are related to many types of human diseases through different biological processes. It is anticipated that precise miRNA-disease association prediction could not only help infer potential disease-related miRNA but also boost human diagnosis and disease prevention. Considering the limitations of previous computational models, a more effective computational model needs to be implemented to predict miRNA-disease associations. In this work, we first constructed a human miRNA-miRNA similarity network utilizing miRNA-miRNA functional similarity data and heterogeneous miRNA Gaussian interaction profile kernel similarities based on the assumption that similar miRNAs with similar functions tend to be associated with similar diseases, and vice versa. Then, we constructed disease-disease similarity using disease semantic information and heterogeneous disease-related interaction data. We proposed a deep ensemble model called DeepMDA that extracts high-level features from similarity information using stacked autoencoders and then predicts miRNA-disease associations by adopting a 3-layer neural network. In addition to five-fold cross-validation, we also proposed another cross-validation method to evaluate the performance of the model. The results show that the proposed model is superior to previous methods with high robustness.

MCMDA: Matrix completion for MiRNA-disease association prediction

Nowadays, researchers have realized that microRNAs (miRNAs) are playing a significant role in many important biological processes and they are closely connected with various complex human diseases. However, since there are too many possible miRNA-disease associations to analyze, it remains difficult to predict the potential miRNAs related to human diseases without a systematic and effective method. In this study, we developed a Matrix Completion for MiRNA-Disease Association prediction model (MCMDA) based on the known miRNA-disease associations in HMDD database. MCMDA model utilized the matrix completion algorithm to update the adjacency matrix of known miRNA-disease associations and furthermore predict the potential associations. To evaluate the performance of MCMDA, we performed leave-one-out cross validation (LOOCV) and 5-fold cross validation to compare MCMDA with three previous classical computational models (RLSMDA, HDMP, and WBSMDA). As a result, MCMDA achieved AUCs of 0.8749 in global LOOCV, 0.7718 in local LOOCV and average AUC of 0.8767+/−0.0011 in 5-fold cross validation. Moreover, the prediction results associated with colon neoplasms, kidney neoplasms, lymphoma and prostate neoplasms were verified. As a consequence, 84%, 86%, 78% and 90% of the top 50 potential miRNAs for these four diseases were respectively confirmed by recent experimental discoveries. Therefore, MCMDA model is superior to the previous models in that it improves the prediction performance although it only depends on the known miRNA-disease associations.

Improved low-rank matrix recovery method for predicting miRNA-disease association

MicroRNAs (miRNAs) performs crucial roles in various human diseases, but miRNA-related pathogenic mechanisms remain incompletely understood. Revealing the potential relationship between miRNAs and diseases is a critical problem in biomedical research. Considering limitation of existing computational approaches, we develop improved low-rank matrix recovery (ILRMR) for miRNA-disease association prediction. ILRMR is a global method that can simultaneously prioritize potential association for all diseases and does not require negative samples. ILRMR can also identify promising miRNAs for investigating diseases without any known related miRNA. By integrating miRNA-miRNA similarity information, disease-disease similarity information, and miRNA family information to matrix recovery, ILRMR performs better than other methods in cross validation and case studies.

RKNNMDA: Ranking-based KNN for MiRNA-Disease Association prediction

Cumulative verified experimental studies have demonstrated that microRNAs (miRNAs) could be closely related with the development and progression of human complex diseases. Based on the assumption that functional similar miRNAs may have a strong correlation with phenotypically similar diseases and vice versa, researchers developed various effective computational models which combine heterogeneous biologic data sets including disease similarity network, miRNA similarity network, and known disease-miRNA association network to identify potential relationships between miRNAs and diseases in biomedical research. Considering the limitations in previous computational study, we introduced a novel computational method of Ranking-based KNN for miRNA-Disease Association prediction (RKNNMDA) to predict potential related miRNAs for diseases, and our method obtained an AUC of 0.8221 based on leave-one-out cross validation. In addition, RKNNMDA was applied to 3 kinds of important human cancers for further performance evaluation. The results showed that 96%, 80% and 94% of predicted top 50 potential related miRNAs for Colon Neoplasms, Esophageal Neoplasms, and Prostate Neoplasms have been confirmed by experimental literatures, respectively. Moreover, RKNNMDA could be used to predict potential miRNAs for diseases without any known miRNAs, and it is anticipated that RKNNMDA would be of great use for novel miRNA-disease association identification.

PBMDA: A novel and effective path-based computational model for miRNA-disease association prediction

In the recent few years, an increasing number of studies have shown that microRNAs (miRNAs) play critical roles in many fundamental and important biological processes. As one of pathogenetic factors, the molecular mechanisms underlying human complex diseases still have not been completely understood from the perspective of miRNA. Predicting potential miRNA-disease associations makes important contributions to understanding the pathogenesis of diseases, developing new drugs, and formulating individualized diagnosis and treatment for diverse human complex diseases. Instead of only depending on expensive and time-consuming biological experiments, computational prediction models are effective by predicting potential miRNA-disease associations, prioritizing candidate miRNAs for the investigated diseases, and selecting those miRNAs with higher association probabilities for further experimental validation. In this study, Path-Based MiRNA-Disease Association (PBMDA) prediction model was proposed by integrating known human miRNA-disease associations, miRNA functional similarity, disease semantic similarity, and Gaussian interaction profile kernel similarity for miRNAs and diseases. This model constructed a heterogeneous graph consisting of three interlinked sub-graphs and further adopted depth-first search algorithm to infer potential miRNA-disease associations. As a result, PBMDA achieved reliable performance in the frameworks of both local and global LOOCV (AUCs of 0.8341 and 0.9169, respectively) and 5-fold cross validation (average AUC of 0.9172). In the cases studies of three important human diseases, 88% (Esophageal Neoplasms), 88% (Kidney Neoplasms) and 90% (Colon Neoplasms) of top-50 predicted miRNAs have been manually confirmed by previous experimental reports from literatures. Through the comparison performance between PBMDA and other previous models in case studies, the reliable performance also demonstrates that PBMDA could serve as a powerful computational tool to accelerate the identification of disease-miRNA associations.

Identification of miRNA-disease associations is considered as a key way for the development of pathology, diagnose and therapy. Computational prediction models contribute to discovering the underlying disease-related miRNAs on a large scale. Based on the assumption that functionally related miRNAs tend to be involved in phenotypically similar disease and vice versa, the model of PBMDA was developed to prioritize the underlying miRNA-disease associations by adopting a special depth-first search algorithm in a heterogeneous graph, which was composed of known miRNA-disease association network, miRNA similarity network, and disease similarity network. Through leave-one-out cross validation and 5-fold cross validation, the promising results demonstrated the effectiveness of the proposed model. We further implemented the case studies of three important human complex diseases, 88%, 88% and 90% of top-50 predicted miRNA-disease associations have been manually confirmed based on recent experimental reports. It is anticipated that PBMDA could prioritize the most potential miRNA-disease associations on a large scale for advancing the progress of biological experiment validation in the future, which could further contribute to the understanding of complex disease mechanisms.

A novel computational model based on super-disease and miRNA for potential miRNA–disease association prediction

Considering the various disadvantages of previous computational models, we proposed a novel computational model based on super-disease and miRNA for potential miRNA–disease association prediction (SDMMDA) to predict potential miRNA–disease associations by integrating known associations, disease semantic similarity, miRNA functional similarity, and Gaussian interaction profile kernel similarity for diseases and miRNAs.

Network Consistency Projection for Human miRNA-Disease Associations Inference

Prediction and confirmation of the presence of disease-related miRNAs is beneficial to understand disease mechanisms at the miRNA level. However, the use of experimental verification to identify disease-related miRNAs is expensive and time-consuming. Effective computational approaches used to predict miRNA-disease associations are highly specific. In this study, we develop the Network Consistency Projection for miRNA-Disease Associations (NCPMDA) method to reveal the potential associations between miRNAs and diseases. NCPMDA is a non-parametric universal network-based method that can simultaneously predict miRNA-disease associations in all diseases but does not require negative samples. NCPMDA can also confirm the presence of miRNAs in isolated diseases (diseases without any known miRNA association). Leave-one-out cross validation and case studies have shown that the predictive performance of NCPMDA is superior over that of previous method.

NLLSS: Predicting Synergistic Drug Combinations Based on Semi-supervised Learning

Fungal infection has become one of the leading causes of hospital-acquired infections with high mortality rates. Furthermore, drug resistance is common for fungus-causing diseases. Synergistic drug combinations could provide an effective strategy to overcome drug resistance. Meanwhile, synergistic drug combinations can increase treatment efficacy and decrease drug dosage to avoid toxicity. Therefore, computational prediction of synergistic drug combinations for fungus-causing diseases becomes attractive. In this study, we proposed similar nature of drug combinations: principal drugs which obtain synergistic effect with similar adjuvant drugs are often similar and vice versa. Furthermore, we developed a novel algorithm termed Network-based Laplacian regularized Least Square Synergistic drug combination prediction (NLLSS) to predict potential synergistic drug combinations by integrating different kinds of information such as known synergistic drug combinations, drug-target interactions, and drug chemical structures. We applied NLLSS to predict antifungal synergistic drug combinations and showed that it achieved excellent performance both in terms of cross validation and independent prediction. Finally, we performed biological experiments for fungal pathogen Candida albicans to confirm 7 out of 13 predicted antifungal synergistic drug combinations. NLLSS provides an efficient strategy to identify potential synergistic antifungal combinations.

Drug combinations represent a promising strategy for overcoming fungal drug resistance and treating complex diseases. There is an urgent need to establish powerful computational methods for systematic prediction of synergistic drug combination on a large scale. Based on the assumption that principal drugs which obtain synergistic effect with similar adjuvant drugs are often similar and vice versa, NLLSS was developed to predict potential synergistic drug combinations by integrating known synergistic drug combinations, unlabeled drug combinations, drug-target interactions, and drug chemical structures. NLLSS has obtained the reliable performance in the cross validation and experimental validations, which indicated that NLLSS has an excellent performance of identifying potential synergistic drug combinations. Out of 13 predicted antifungal synergistic drug combinations, 7 candidates were experimentally confirmed. It is anticipated that NLLSS would be an important and useful resource by providing a new strategy to identify potential synergistic antifungal combinations, explore new indications of existing drugs, and provide useful insights into the underlying molecular mechanisms of synergistic drug combinations.

WBSMDA: Within and Between Score for MiRNA-Disease Association prediction

Increasing evidences have indicated that microRNAs (miRNAs) are functionally associated with the development and progression of various complex human diseases. However, the roles of miRNAs in multiple biological processes or various diseases and their underlying molecular mechanisms still have not been fully understood yet. Predicting potential miRNA-disease associations by integrating various heterogeneous biological datasets is of great significance to the biomedical research. Computational methods could obtain potential miRNA-disease associations in a short time, which significantly reduce the experimental time and cost. Considering the limitations in previous computational methods, we developed the model of Within and Between Score for MiRNA-Disease Association prediction (WBSMDA) to predict potential miRNAs associated with various complex diseases. WBSMDA could be applied to the diseases without any known related miRNAs. The AUC of 0.8031 based on Leave-one-out cross validation has demonstrated its reliable performance. WBSMDA was further applied to Colon Neoplasms, Prostate Neoplasms, and Lymphoma for the identification of their potential related miRNAs. As a result, 90%, 84%, and 80% of predicted miRNA-disease pairs in the top 50 prediction list for these three diseases have been confirmed by recent experimental literatures, respectively. It is anticipated that WBSMDA would be a useful resource for potential miRNA-disease association identification.

Bioinformatic tools for microRNA dissection

Recently, microRNAs (miRNAs) have emerged as important elements of gene regulatory networks. MiRNAs are endogenous single-stranded non-coding RNAs (~22-nt long) that regulate gene expression at the post-transcriptional level. Through pairing with mRNA, miRNAs can down-regulate gene expression by inhibiting translation or stimulating mRNA degradation. In some cases they can also up-regulate the expression of a target gene. MiRNAs influence a variety of cellular pathways that range from development to carcinogenesis. The involvement of miRNAs in several human diseases, particularly cancer, makes them potential diagnostic and prognostic biomarkers. Recent technological advances, especially high-throughput sequencing, have led to an exponential growth in the generation of miRNA-related data. A number of bioinformatic tools and databases have been devised to manage this growing body of data. We analyze 129 miRNA tools that are being used in diverse areas of miRNA research, to assist investigators in choosing the most appropriate tools for their needs.

miREFRWR: a novel disease-related microRNA-environmental factor interactions prediction method

miREFRWR was developed to uncover the hidden disease-related miRNA–EF interactions by implementing random walks on an miRNA similarity network and EF similarity network, respectively.

KATZLDA: KATZ measure for the lncRNA-disease association prediction

Accumulating experimental studies have demonstrated important associations between alterations and dysregulations of lncRNAs and the development and progression of various complex human diseases. Developing effective computational models to integrate vast amount of heterogeneous biological data for the identification of potential disease-lncRNA associations has become a hot topic in the fields of human complex diseases and lncRNAs, which could benefit lncRNA biomarker detection for disease diagnosis, treatment, and prevention. Considering the limitations in previous computational methods, the model of KATZ measure for LncRNA-Disease Association prediction (KATZLDA) was developed to uncover potential lncRNA-disease associations by integrating known lncRNA-disease associations, lncRNA expression profiles, lncRNA functional similarity, disease semantic similarity, and Gaussian interaction profile kernel similarity. KATZLDA could work for diseases without known related lncRNAs and lncRNAs without known associated diseases. KATZLDA obtained reliable AUCs of 7175, 0.7886, 0.7719 in the local and global leave-one-out cross validation and 5-fold cross validation, respectively, significantly improving previous classical methods. Furthermore, case studies of colon, gastric, and renal cancer were implemented and 60% of top 10 predictions have been confirmed by recent biological experiments. It is anticipated that KATZLDA could be an important resource with potential values for biomedical researches.

Identifying human microRNA–disease associations by a new diffusion-based method

Identifying the microRNA–disease relationship is vital for investigating the pathogenesis of various diseases. However, experimental verification of disease-related microRNAs remains considerable challenge to many researchers, particularly for the fact that numerous new microRNAs are discovered every year. As such, development of computational methods for disease-related microRNA prediction has recently gained eminent attention. In this paper, first, we construct a miRNA functional network and a disease similarity network by integrating different information sources. Then, we further introduce a new diffusion-based method (NDBM) to explore global network similarity for miRNA–disease association inference. Even though known miRNA–disease associations in the database are rare, NDBM still achieves an area under the ROC curve (AUC) of 85.62% in the leave-one-out cross-validation in improving the prediction accuracy of previous methods significantly. Moreover, our method is applicable to diseases with no known related miRNAs as well as new miRNAs with unknown target diseases. Some associations who strongly predicted by our method are confirmed by public databases. These superior performances suggest that NDBM could be an effective and important tool for biomedical research.

Similarity computation strategies in the microRNA-disease network: a survey

Various microRNAs have been demonstrated to play roles in a number of human diseases. Several microRNA-disease network reconstruction methods have been used to describe the association from a systems biology perspective. The key problem for the network is the similarity computation model. In this article, we reviewed the main similarity computation methods and discussed these methods and future works. This survey may prompt and guide systems biology and bioinformatics researchers to build more perfect microRNA-disease associations and may make the network relationship clear for medical researchers.

Constructing lncRNA functional similarity network based on lncRNA-disease associations and disease semantic similarity

Increasing evidence has indicated that plenty of lncRNAs play important roles in many critical biological processes. Developing powerful computational models to construct lncRNA functional similarity network based on heterogeneous biological datasets is one of the most important and popular topics in the fields of both lncRNAs and complex diseases. Functional similarity network consturction could benefit the model development for both lncRNA function inference and lncRNA-disease association identification. However, little effort has been attempted to analysis and calculate lncRNA functional similarity on a large scale. In this study, based on the assumption that functionally similar lncRNAs tend to be associated with similar diseases, we developed two novel lncRNA functional similarity calculation models (LNCSIM). LNCSIM was evaluated by introducing similarity scores into the model of Laplacian Regularized Least Squares for LncRNA–Disease Association (LRLSLDA) for lncRNA-disease association prediction. As a result, new predictive models improved the performance of LRLSLDA in the leave-one-out cross validation of various known lncRNA-disease associations datasets. Furthermore, some of the predictive results for colorectal cancer and lung cancer were verified by independent biological experimental studies. It is anticipated that LNCSIM could be a useful and important biological tool for human disease diagnosis, treatment, and prevention.

A miRNA-driven inference model to construct potential drug-disease associations for drug repositioning

Increasing evidence discovered that the inappropriate expression of microRNAs (miRNAs) will lead to many kinds of complex diseases and drugs can regulate the expression level of miRNAs. Therefore human diseases may be treated by targeting some specific miRNAs with drugs, which provides a new perspective for drug repositioning. However, few studies have attempted to computationally predict associations between drugs and diseases via miRNAs for drug repositioning. In this paper, we developed an inference model to achieve this aim by combining experimentally supported drug-miRNA associations and miRNA-disease associations with the assumption that drugs will form associations with diseases when they share some significant miRNA partners. Experimental results showed excellent performance of our model. Case studies demonstrated that some of the strongly predicted drug-disease associations can be confirmed by the publicly accessible database CTD (www.ctdbase.org), which indicated the usefulness of our inference model. Moreover, candidate miRNAs as molecular hypotheses underpinning the associations were listed to guide future experiments. The predicted results were released for further studies. We expect that this study will provide help in our understanding of drug-disease association prediction and in the roles of miRNAs in drug repositioning.

Computational prediction of microRNA networks incorporating environmental toxicity and disease etiology

MicroRNAs (miRNAs) play important roles in multiple biological processes and have attracted much scientific attention recently. Their expression can be altered by environmental factors (EFs), which are associated with many diseases. Identification of the phenotype-genotype relationships among miRNAs, EFs, and diseases at the network level will help us to better understand toxicology mechanisms and disease etiologies. In this study, we developed a computational systems toxicology framework to predict new associations among EFs, miRNAs and diseases by integrating EF structure similarity and disease phenotypic similarity. Specifically, three comprehensive bipartite networks: EF-miRNA, EF-disease and miRNA-disease associations, were constructed to build predictive models. The areas under the receiver operating characteristic curves using 10-fold cross validation ranged from 0.686 to 0.910. Furthermore, we successfully inferred novel EF-miRNA-disease networks in two case studies for breast cancer and cigarette smoke. Collectively, our methods provide a reliable and useful tool for the study of chemical risk assessment and disease etiology involving miRNAs.

Semi-supervised learning for potential human microRNA-disease associations inference

MicroRNAs play critical role in the development and progression of various diseases. Predicting potential miRNA-disease associations from vast amount of biological data is an important problem in the biomedical research. Considering the limitations in previous methods, we developed Regularized Least Squares for MiRNA-Disease Association (RLSMDA) to uncover the relationship between diseases and miRNAs. RLSMDA can work for diseases without known related miRNAs. Furthermore, it is a semi-supervised (does not need negative samples) and global method (prioritize associations for all the diseases simultaneously). Based on leave-one-out cross validation, reliable AUC have demonstrated the reliable performance of RLSMDA. We also applied RLSMDA to Hepatocellular cancer and Lung cancer and implemented global prediction for all the diseases simultaneously. As a result, 80% (Hepatocellular cancer) and 84% (Lung cancer) of top 50 predicted miRNAs and 75% of top 20 potential associations based on global prediction have been confirmed by biological experiments. We also applied RLSMDA to diseases without known related miRNAs in golden standard dataset. As a result, in the top 3 potential related miRNA list predicted by RLSMDA for 32 diseases, 34 disease-miRNA associations were successfully confirmed by experiments. It is anticipated that RLSMDA would be a useful bioinformatics resource for biomedical researches.

Novel human lncRNA-disease association inference based on lncRNA expression profiles

MOTIVATION

More and more evidences have indicated that long-non-coding RNAs (lncRNAs) play critical roles in many important biological processes. Therefore, mutations and dysregulations of these lncRNAs would contribute to the development of various complex diseases. Developing powerful computational models for potential disease-related lncRNAs identification would benefit biomarker identification and drug discovery for human disease diagnosis, treatment, prognosis and prevention.

RESULTS

In this article, we proposed the assumption that similar diseases tend to be associated with functionally similar lncRNAs. Then, we further developed the method of Laplacian Regularized Least Squares for LncRNA-Disease Association (LRLSLDA) in the semisupervised learning framework. Although known disease-lncRNA associations in the database are rare, LRLSLDA still obtained an AUC of 0.7760 in the leave-one-out cross validation, significantly improving the performance of previous methods. We also illustrated the performance of LRLSLDA is not sensitive (even robust) to the parameters selection and it can obtain a reliable performance in all the test classes. Plenty of potential disease-lncRNA associations were publicly released and some of them have been confirmed by recent results in biological experiments. It is anticipated that LRLSLDA could be an effective and important biological tool for biomedical research.

AVAILABILITY

The code of LRLSLDA is freely available at http://asdcd.amss.ac.cn/Software/Details/2.

Expression of microRNA-30c and its target genes in human osteoblastic cells by nano-bioglass ceramic-treatment

Osteoblast differentiation is tightly regulated by post transcriptional regulators such as microRNAs (miRNAs). Several bioactive materials including nano-bioglass ceramic particles (nBGC) influence differentiation of the osteoblasts, but the molecular mechanisms of nBGC-stimulation of osteoblast differentiation via miRNAs are not yet determined. In this study, we identified that nBGC-treatment stimulated miR-30c expression in human osteoblastic cells (MG63). The bioinformatics tools identified its regulatory network, molecular function, biological processes and its target genes involved in negative regulation of osteoblast differentiation. TGIF2 and HDAC4 were found to be its putative target genes and their expression was down regulated by nBGC-treatment in MG63 cells. Thus, this study advances our understanding of nBGC action on bone cells and supports utilization of nBGC in bone tissue engineering.