PhenomiR: a knowledgebase for microRNA expression in diseases and biological processes

TIWMFLP: Two-Tier Interactive Weighted Matrix Factorization and Label Propagation Based on Similarity Matrix Fusion for Drug-Disease Association Prediction

Accurately identifying new therapeutic uses for drugs is crucial for advancing pharmaceutical research and development. Matrix factorization is often used in association prediction due to its simplicity and high interpretability. However, existing matrix factorization models do not enable real-time interaction between molecular feature matrices and similarity matrices, nor do they consider the geometric structure of the matrices. Additionally, efficiently integrating multisource data remains a significant challenge. To address these issues, we propose a two-tier interactive weighted matrix factorization and label propagation model based on similarity matrix fusion (TIWMFLP) to assist in personalized treatment. First, we calculate the Gaussian and Laplace kernel similarities for drugs and diseases using known drug-disease associations. We then introduce a new multisource similarity fusion method, called similarity matrix fusion (SMF), to integrate these drug/disease similarities. SMF not only considers the different contributions represented by each neighbor but also incorporates drug-disease association information to enhance the contextual topological relationships and potential features of each drug/disease node in the network. Second, we innovatively developed a two-tier interactive weighted matrix factorization (TIWMF) method to process three biological networks. This method realizes for the first time the real-time interaction between the drug/disease feature matrix and its similarity matrix, allowing for a better capture of the complex relationships between drugs and diseases. Additionally, the weighted matrix of the drug/disease similarity matrix is introduced to preserve the underlying structure of the similarity matrix. Finally, the label propagation algorithm makes predictions based on the three updated biological networks. Experimental outcomes reveal that TIWMFLP consistently surpasses state-of-the-art models on four drug-disease data sets, two small molecule-miRNA data sets, and one miRNA-disease data set.

Shedding Light on the Molecular Diversities of miRNA in Cancer- an Exquisite Mini Review

Short non-coding ribonucleic acids are also known as "Micro ribonucleic acids (miRNAs)". The miRNAs make a contribution to the regulation of genes and mitigation of cancer cell growth in humans. miRNAs play a significant role in several BPs, namely apoptosis, cell cycle progression, and development. It is well-recognized that miRNAs are crucial for the tumors' growth and also serve as Tumor Suppressors (TSs) or oncogenes. As miRNAs also act as an effective tumor suppressor, studying the molecular diversities of the miRNAs makes way to minimize cancer progression and the corresponding death rates in the future. Therefore, miRNAs along with their Biological Processes (BPs) and molecular diversities are thoroughly researched in this paper. Consequently, miRNAs particularly target their 3' UnTranslated Region (3'-UTR) for controlling the target mRNAs' stability and protein translation. So, this study also expresses the impact of microRNA variants in various cancer cells, namely Breast cancer, Gastric or stomach cancer, ovarian cancer, and lymphocytic leukemia. Furthermore, the database named PhenomiR and commercial kits that are used in the miRNA data analysis are discussed in this article to provide extensive knowledge about the molecular diversity analysis of miRNA and their influences on cancerous cells.

Microparticle Mediated Delivery of Apelin Improves Heart Function in Post Myocardial Infarction Mice

BACKGROUND

Apelin is an endogenous prepropeptide that regulates cardiac homeostasis and various physiological processes. Intravenous injection has been shown to improve cardiac contractility in patients with heart failure. However, its short half-life prevents studying its impact on left ventricular remodeling in the long term. Here, we aim to study whether microparticle-mediated slow release of apelin improves heart function and left ventricular remodeling in mice with myocardial infarction (MI).

METHODS

A cardiac patch was fabricated by embedding apelin-containing microparticles in a fibrin gel scaffold. MI was induced via permanent ligation of the left anterior descending coronary artery in adult C57BL/6J mice followed by epicardial patch placement immediately after (acute MI) or 28 days (chronic MI) post-MI. Four groups were included in this study, namely sham, MI, MI plus empty microparticle-embedded patch treatment, and MI plus apelin-containing microparticle-embedded patch treatment. Cardiac function was assessed by transthoracic echocardiography. Cardiomyocyte morphology, apoptosis, and cardiac fibrosis were evaluated by histology. Cardioprotective pathways were determined by RNA sequencing, quantitative polymerase chain reaction, and Western blot.

RESULTS

The level of endogenous apelin was largely reduced in the first 7 days after MI induction and it was normalized by day 28. Apelin-13 encapsulated in poly(lactic-co-glycolic acid) microparticles displayed a sustained release pattern for up to 28 days. Treatment with apelin-containing microparticle-embedded patch inhibited cardiac hypertrophy and reduced scar size in both acute and chronic MI models, which is associated with improved cardiac function. Data from cellular and molecular analyses showed that apelin inhibits the activation and proliferation of cardiac fibroblasts by preventing transforming growth factor-β-mediated activation of Smad2/3 (supporessor of mothers against decapentaplegic 2/3) and downstream profibrotic gene expression.

CONCLUSIONS

Poly(lactic-co-glycolic acid) microparticles prolonged the apelin release time in the mouse hearts. Epicardial delivery of the apelin-containing microparticle-embedded patch protects mice from both acute and chronic MI-induced cardiac dysfunction, inhibits cardiac fibrosis, and improves left ventricular remodeling.

HTFSMMA: Higher-Order Topological Guided Small Molecule-MicroRNA Associations Prediction

Small molecules (SMs) play a pivotal role in regulating microRNAs (miRNAs). Existing prediction methods for associations between SM-miRNA have overlooked crucial aspects: the incorporation of local topological features between nodes, which represent either SMs or miRNAs, and the effective fusion of node features with topological features. This study introduces a novel approach, termed high-order topological features for SM-miRNA association prediction (HTFSMMA), which specifically addresses these limitations. Initially, an association graph is formed by integrating SM-miRNA association data, SM similarity, and miRNA similarity. Subsequently, we focus on the local information of links and propose target neighborhood graph convolutional network for extracting local topological features. Then, HTFSMMA employs graph attention networks to amalgamate these local features, thereby establishing a platform for the acquisition of high-order features through random walks. Finally, the extracted features are integrated into the multilayer perceptron to derive the association prediction scores. To demonstrate the performance of HTFSMMA, we conducted comprehensive evaluations including five-fold cross-validation, leave-one-out cross-validation (LOOCV), SM-fixed local LOOCV, and miRNA-fixed local LOOCV. The area under receiver operating characteristic curve values were 0.9958 ± 0.0024 (0.8722 ± 0.0021), 0.9986 (0.9504), 0.9974 (0.9111), and 0.9977 (0.9074), respectively. Our findings demonstrate the superior performance of HTFSMMA over existing approaches. In addition, three case studies and the DeLong test have confirmed the effectiveness of the proposed method. These results collectively underscore the significance of HTFSMMA in facilitating the inference of associations between SMs and miRNAs.

Heterogeneous graph inference with range constrainted L2,1-collaborative matrix factorization for small molecule-miRNA association prediction

MicroRNAs (miRNAs) play a vital role in regulating gene expression and various biological processes. As a result, they have been identified as effective targets for small molecule (SM) drugs in disease treatment. Heterogeneous graph inference stands as a classical approach for predicting SM-miRNA associations, showcasing commendable convergence accuracy and speed. However, most existing methods do not adequately address the inherent sparsity in SM-miRNA association networks, and imprecise SM/miRNA similarity metrics reduce the accuracy of predicting SM-miRNA associations. In this research, we proposed a heterogeneous graph inference with range constrained L2,1-collaborative matrix factorization (HGIRCLMF) method to predict potential SM-miRNA associations. First, we computed the multi-source similarities of SM/miRNA and integrated these similarity information into a comprehensive SM/miRNA similarity. This step improved the accuracy of SM and miRNA similarity, ensuring reliability for the subsequent inference of the heterogeneity map. Second, we used a range constrained L2,1-collaborative matrix factorization (RCLMF) model to pre-populate the SM-miRNA association matrix with missing values. In this step, we developed a novel matrix decomposition method that enhances the robustness and formative nature of SM-miRNA edges between SM networks and miRNA networks. Next, we built a well-established SM-miRNA heterogeneous network utilizing the processed biological information. Finally, HGIRCLMF used this network data to infer unknown association pair scores. We implemented four cross-validation experiments on two distinct datasets, and HGIRCLMF acquired the highest areas under the curve, surpassing six state-of-the-art computational approaches. Furthermore, we performed three case studies to validate the predictive power of our method in practical application.

A Survey of Deep Learning for Detecting miRNA- Disease Associations: Databases, Computational Methods, Challenges, and Future Directions

MicroRNAs (miRNAs) are an important class of non-coding RNAs that play an essential role in the occurrence and development of various diseases. Identifying the potential miRNA-disease associations (MDAs) can be beneficial in understanding disease pathogenesis. Traditional laboratory experiments are expensive and time-consuming. Computational models have enabled systematic large-scale prediction of potential MDAs, greatly improving the research efficiency. With recent advances in deep learning, it has become an attractive and powerful technique for uncovering novel MDAs. Consequently, numerous MDA prediction methods based on deep learning have emerged. In this review, we first summarize publicly available databases related to miRNAs and diseases for MDA prediction. Next, we outline commonly used miRNA and disease similarity calculation and integration methods. Then, we comprehensively review the 48 existing deep learning-based MDA computation methods, categorizing them into classical deep learning and graph neural network-based techniques. Subsequently, we investigate the evaluation methods and metrics that are frequently used to assess MDA prediction performance. Finally, we discuss the performance trends of different computational methods, point out some problems in current research, and propose 9 potential future research directions. Data resources and recent advances in MDA prediction methods are summarized in the GitHub repository https://github.com/sheng-n/DL-miRNA-disease-association-methods.

ReHoGCNES-MDA: prediction of miRNA-disease associations using homogenous graph convolutional networks based on regular graph with random edge sampler

Numerous investigations increasingly indicate the significance of microRNA (miRNA) in human diseases. Hence, unearthing associations between miRNA and diseases can contribute to precise diagnosis and efficacious remediation of medical conditions. The detection of miRNA-disease linkages via computational techniques utilizing biological information has emerged as a cost-effective and highly efficient approach. Here, we introduced a computational framework named ReHoGCNES, designed for prospective miRNA-disease association prediction (ReHoGCNES-MDA). This method constructs homogenous graph convolutional network with regular graph structure (ReHoGCN) encompassing disease similarity network, miRNA similarity network and known MDA network and then was tested on four experimental tasks. A random edge sampler strategy was utilized to expedite processes and diminish training complexity. Experimental results demonstrate that the proposed ReHoGCNES-MDA method outperforms both homogenous graph convolutional network and heterogeneous graph convolutional network with non-regular graph structure in all four tasks, which implicitly reveals steadily degree distribution of a graph does play an important role in enhancement of model performance. Besides, ReHoGCNES-MDA is superior to several machine learning algorithms and state-of-the-art methods on the MDA prediction. Furthermore, three case studies were conducted to further demonstrate the predictive ability of ReHoGCNES. Consequently, 93.3% (breast neoplasms), 90% (prostate neoplasms) and 93.3% (prostate neoplasms) of the top 30 forecasted miRNAs were validated by public databases. Hence, ReHoGCNES-MDA might serve as a dependable and beneficial model for predicting possible MDAs.

HMDD v4.0: a database for experimentally supported human microRNA-disease associations

MicroRNAs (miRNAs) are a class of important small non-coding RNAs with critical molecular functions in almost all biological processes, and thus, they play important roles in disease diagnosis and therapy. Human MicroRNA Disease Database (HMDD) represents an important and comprehensive resource for biomedical researchers in miRNA-related medicine. Here, we introduce HMDD v4.0, which curates 53530 miRNA-disease association entries from literatures. In comparison to HMDD v3.0 released five years ago, HMDD v4.0 contains 1.5 times more entries. In addition, some new categories have been curated, including exosomal miRNAs implicated in diseases, virus-encoded miRNAs involved in human diseases, and entries containing miRNA-circRNA interactions. We also curated sex-biased miRNAs in diseases. Furthermore, in a case study, disease similarity analysis successfully revealed that sex-biased miRNAs related to developmental anomalies are associated with a number of human diseases with sex bias. HMDD can be freely visited at http://www.cuilab.cn/hmdd.

Comparative Analysis of Published Database Predicting MicroRNA Binding in 3'UTR of mRNA in Diverse Species

BACKGROUND

Micro-RNAs are endogenous non-coding RNA moieties of 22-27 nucleotides that play a crucial role in the regulation of various biological processes and make them useful prognostic and diagnostic biomarkers. Discovery and experimental validation of miRNA is a laborious and time-consuming process. For early prediction, multiple bioinformatics databases are available for miRNA target prediction; however, their utility can confuse amateur researchers in selecting the most appropriate tools for their study.

OBJECTIVE

This descriptive review aimed to analyse the usability of the existing database based on the following criteria: accessibility, efficiency, interpretability, updatability, and flexibility for miRNA target prediction of 3'UTR of mRNA in diverse species so that the researchers can utilize the database most appropriate to their research.

METHODS

A systematic literature search was performed in PubMed, Google Scholar and Scopus databases up to November 2022. ≥10,000 articles found online, including ⁓130 miRNA tools, which contain various information on miRNA. Out of them, 31 databases that provide information on validated 3'UTR miRNAs target databases were included and analysed in this review.

RESULTS

These miRNA database tools are being used in varied areas of biological research to select the most suitable miRNA for their experimental validation. These databases, updated until the year 2021, consist of miRNA-related data from humans, animals, mice, plants, viruses etc. They contain 525-29806351 data entries, and information from most databases is freely available on the online platform.

CONCLUSION

Reviewed databases provide significant information, but not all information is accurate or up-to-date. Therefore, Diana-TarBase and miRWalk are the most comprehensive and up-to-date databases.

ESOMIR: a curated database of biomarker genes and miRNAs associated with esophageal cancer

'Esophageal cancer' (EC) is a highly aggressive and deadly complex disease. It comprises two types, esophageal adenocarcinoma (EAC) and esophageal squamous cell carcinoma (ESCC), with Barrett's esophagus (BE) being the only known precursor. Recent research has revealed that microRNAs (miRNAs) play a crucial role in the development, prognosis and treatment of EC and are involved in various human diseases. Biological databases have become essential for cancer research as they provide information on genes, proteins, pathways and their interactions. These databases collect, store and manage large amounts of molecular data, which can be used to identify patterns, predict outcomes and generate hypotheses. However, no comprehensive database exists for EC and miRNA relationships. To address this gap, we developed a dynamic database named 'ESOMIR (miRNA in esophageal cancer) (https://esomir.dqweilab-sjtu.com)', which includes information about targeted genes and miRNAs associated with EC. The database uses analysis and prediction methods, including experimentally endorsed miRNA(s) information. ESOMIR is a user-friendly interface that allows easy access to EC-associated data by searching for miRNAs, target genes, sequences, chromosomal positions and associated signaling pathways. The search modules are designed to provide specific data access to users based on their requirements. Additionally, the database provides information about network interactions, signaling pathways and region information of chromosomes associated with the 3'untranslated region (3'UTR) or 5'UTR and exon sites. Users can also access energy levels of specific miRNAs with targeted genes. A fuzzy term search is included in each module to enhance the ease of use for researchers. ESOMIR can be a valuable tool for researchers and clinicians to gain insight into EC, including identifying biomarkers and treatments for this aggressive tumor. Database URL https://esomir.dqweilab-sjtu.com.

Identifying potential small molecule-miRNA associations via Robust PCA based on γ-norm regularization

Dysregulation of microRNAs (miRNAs) is closely associated with refractory human diseases, and the identification of potential associations between small molecule (SM) drugs and miRNAs can provide valuable insights for clinical treatment. Existing computational techniques for inferring potential associations suffer from limitations in terms of accuracy and efficiency. To address these challenges, we devise a novel predictive model called RPCA$\Gamma $NR, in which we propose a new Robust principal component analysis (PCA) framework based on $\gamma $-norm and $l_{2,1}$-norm regularization and design an Augmented Lagrange Multiplier method to optimize it, thereby deriving the association scores. The Gaussian Interaction Profile Kernel Similarity is calculated to capture the similarity information of SMs and miRNAs in known associations. Through extensive evaluation, including Cross Validation Experiments, Independent Validation Experiment, Efficiency Analysis, Ablation Experiment, Matrix Sparsity Analysis, and Case Studies, RPCA$\Gamma $NR outperforms state-of-the-art models concerning accuracy, efficiency and robustness. In conclusion, RPCA$\Gamma $NR can significantly streamline the process of determining SM-miRNA associations, thus contributing to advancements in drug development and disease treatment.

Neighborhood-based inference and restricted Boltzmann machine for small molecule-miRNA associations prediction

Background

A growing number of experiments have shown that microRNAs (miRNAs) can be used as target of small molecules (SMs) to regulate gene expression for treating diseases. Therefore, identifying SM-related miRNAs is helpful for the treatment of diseases in the domain of medical investigation.

Methods

This article presents a new computational model, called NIRBMSMMA (neighborhood-based inference (NI) and restricted Boltzmann machine (RBM)), which we developed to identify potential small molecule-miRNA associations (NIRBMSMMA). First, grounded on known SM-miRNAs associations, SM similarity and miRNA similarity, NI was used to predict score of an unknown SM-miRNA pair by reckoning the sum of known associations between neighbors of the SM (miRNA) and the miRNA (SM). Second, utilizing a two-layered generative stochastic artificial neural network, RBM was used to predict SM-miRNA association by learning potential probability distribution from known SM-miRNA associations. At last, an ensemble learning model was conducted to combine NI and RBM for identifying potential SM-miRNA associations.

Results

Furthermore, we conducted global leave one out cross validation (LOOCV), miRNA-fixed LOOCV, SM-fixed LOOCV and five-fold cross validation to assess performance of NIRBMSMMA based on three datasets. Results showed that NIRBMSMMA obtained areas under the curve (AUC) of 0.9912, 0.9875, 0.8376 and 0.9898 ± 0.0009 under global LOOCV, miRNA-fixed LOOCV, SM-fixed LOOCV and five-fold cross validation based on dataset 1, respectively. For dataset 2, the AUCs are 0.8645, 0.8720, 0.7066 and 0.8547 ± 0.0046 in turn. For dataset 3, the AUCs are 0.9884, 0.9802, 0.8239 and 0.9870 ± 0.0015 in turn. Also, we conducted case studies to further assess the predictive performance of NIRBMSMMA. These results illustrated the proposed model is a useful tool in predicting potential SM-miRNA associations.

Predicting potential small molecule-miRNA associations utilizing truncated schatten p-norm

MicroRNAs (miRNAs) have significant implications in diverse human diseases and have proven to be effectively targeted by small molecules (SMs) for therapeutic interventions. However, current SM-miRNA association prediction models do not adequately capture SM/miRNA similarity. Matrix completion is an effective method for association prediction, but existing models use nuclear norm instead of rank function, which has some drawbacks. Therefore, we proposed a new approach for predicting SM-miRNA associations by utilizing the truncated schatten p-norm (TSPN). First, the SM/miRNA similarity was preprocessed by incorporating the Gaussian interaction profile kernel similarity method. This identified more SM/miRNA similarities and significantly improved the SM-miRNA prediction accuracy. Next, we constructed a heterogeneous SM-miRNA network by combining biological information from three matrices and represented the network with its adjacency matrix. Finally, we constructed the prediction model by minimizing the truncated schatten p-norm of this adjacency matrix and we developed an efficient iterative algorithmic framework to solve the model. In this framework, we also used a weighted singular value shrinkage algorithm to avoid the problem of excessive singular value shrinkage. The truncated schatten p-norm approximates the rank function more closely than the nuclear norm, so the predictions are more accurate. We performed four different cross-validation experiments on two separate datasets, and TSPN outperformed various most advanced methods. In addition, public literature confirms a large number of predictive associations of TSPN in four case studies. Therefore, TSPN is a reliable model for SM-miRNA association prediction.

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

BACKGROUND

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

RESULTS

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

CONCLUSION

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

Prediction of small molecule drug-miRNA associations based on GNNs and CNNs

MicroRNAs (miRNAs) play a crucial role in various biological processes and human diseases, and are considered as therapeutic targets for small molecules (SMs). Due to the time-consuming and expensive biological experiments required to validate SM-miRNA associations, there is an urgent need to develop new computational models to predict novel SM-miRNA associations. The rapid development of end-to-end deep learning models and the introduction of ensemble learning ideas provide us with new solutions. Based on the idea of ensemble learning, we integrate graph neural networks (GNNs) and convolutional neural networks (CNNs) to propose a miRNA and small molecule association prediction model (GCNNMMA). Firstly, we use GNNs to effectively learn the molecular structure graph data of small molecule drugs, while using CNNs to learn the sequence data of miRNAs. Secondly, since the black-box effect of deep learning models makes them difficult to analyze and interpret, we introduce attention mechanisms to address this issue. Finally, the neural attention mechanism allows the CNNs model to learn the sequence data of miRNAs to determine the weight of sub-sequences in miRNAs, and then predict the association between miRNAs and small molecule drugs. To evaluate the effectiveness of GCNNMMA, we implement two different cross-validation (CV) methods based on two different datasets. Experimental results show that the cross-validation results of GCNNMMA on both datasets are better than those of other comparison models. In a case study, Fluorouracil was found to be associated with five different miRNAs in the top 10 predicted associations, and published experimental literature confirmed that Fluorouracil is a metabolic inhibitor used to treat liver cancer, breast cancer, and other tumors. Therefore, GCNNMMA is an effective tool for mining the relationship between small molecule drugs and miRNAs relevant to diseases.

AMCSMMA: Predicting Small Molecule-miRNA Potential Associations Based on Accurate Matrix Completion

Exploring potential associations between small molecule drugs (SMs) and microRNAs (miRNAs) is significant for drug development and disease treatment. Since biological experiments are expensive and time-consuming, we propose a computational model based on accurate matrix completion for predicting potential SM-miRNA associations (AMCSMMA). Initially, a heterogeneous SM-miRNA network is constructed, and its adjacency matrix is taken as the target matrix. An optimization framework is then proposed to recover the target matrix with the missing values by minimizing its truncated nuclear norm, an accurate, robust, and efficient approximation to the rank function. Finally, we design an effective two-step iterative algorithm to solve the optimization problem and obtain the prediction scores. After determining the optimal parameters, we conduct four kinds of cross-validation experiments based on two datasets, and the results demonstrate that AMCSMMA is superior to the state-of-the-art methods. In addition, we implement another validation experiment, in which more evaluation metrics in addition to the AUC are introduced and finally achieve great results. In two types of case studies, a large number of SM-miRNA pairs with high predictive scores are confirmed by the published experimental literature. In summary, AMCSMMA has superior performance in predicting potential SM-miRNA associations, which can provide guidance for biological experiments and accelerate the discovery of new SM-miRNA associations.

Combining non-negative matrix factorization with graph Laplacian regularization for predicting drug-miRNA associations based on multi-source information fusion

Increasing evidences suggest that miRNAs play a key role in the occurrence and progression of many complex human diseases. Therefore, targeting dysregulated miRNAs with small molecule drugs in the clinical has become a new treatment. Nevertheless, it is high cost and time-consuming for identifying miRNAs-targeted with drugs by biological experiments. Thus, more reliable computational method for identification associations of drugs with miRNAs urgently need to be developed. In this study, we proposed an efficient method, called GNMFDMA, to predict potential associations of drug with miRNA by combining graph Laplacian regularization with non-negative matrix factorization. We first calculated the overall similarity matrices of drugs and miRNAs according to the collected different biological information. Subsequently, the new drug-miRNA association adjacency matrix was reformulated based on the K nearest neighbor profiles so as to put right the false negative associations. Finally, graph Laplacian regularization collaborative non-negative matrix factorization was used to calculate the association scores of drugs with miRNAs. In the cross validation, GNMFDMA obtains AUC of 0.9193, which outperformed the existing methods. In addition, case studies on three common drugs (i.e., 5-Aza-CdR, 5-FU and Gemcitabine), 30, 31 and 34 of the top-50 associations inferred by GNMFDMA were verified. These results reveal that GNMFDMA is a reliable and efficient computational approach for identifying the potential drug-miRNA associations.

Matrix reconstruction with reliable neighbors for predicting potential MiRNA-disease associations

Numerous experimental studies have indicated that alteration and dysregulation in mircroRNAs (miRNAs) are associated with serious diseases. Identifying disease-related miRNAs is therefore an essential and challenging task in bioinformatics research. Computational methods are an efficient and economical alternative to conventional biomedical studies and can reveal underlying miRNA-disease associations for subsequent experimental confirmation with reasonable confidence. Despite the success of existing computational approaches, most of them only rely on the known miRNA-disease associations to predict associations without adding other data to increase the prediction accuracy, and they are affected by issues of data sparsity. In this paper, we present MRRN, a model that combines matrix reconstruction with node reliability to predict probable miRNA-disease associations. In MRRN, the most reliable neighbors of miRNA and disease are used to update the original miRNA-disease association matrix, which significantly reduces data sparsity. Unknown miRNA-disease associations are reconstructed by aggregating the most reliable first-order neighbors to increase prediction accuracy by representing the local and global structure of the heterogeneous network. Five-fold cross-validation of MRRN produced an area under the curve (AUC) of 0.9355 and area under the precision-recall curve (AUPR) of 0.2646, values that were greater than those produced by comparable models. Two different types of case studies using three diseases were conducted to demonstrate the accuracy of MRRN, and all top 30 predicted miRNAs were verified.

miRdisNET: Discovering microRNA biomarkers that are associated with diseases utilizing biological knowledge-based machine learning

During recent years, biological experiments and increasing evidence have shown that microRNAs play an important role in the diagnosis and treatment of human complex diseases. Therefore, to diagnose and treat human complex diseases, it is necessary to reveal the associations between a specific disease and related miRNAs. Although current computational models based on machine learning attempt to determine miRNA-disease associations, the accuracy of these models need to be improved, and candidate miRNA-disease relations need to be evaluated from a biological perspective. In this paper, we propose a computational model named miRdisNET to predict potential miRNA-disease associations. Specifically, miRdisNET requires two types of data, i.e., miRNA expression profiles and known disease-miRNA associations as input files. First, we generate subsets of specific diseases by applying the grouping component. These subsets contain miRNA expressions with class labels associated with each specific disease. Then, we assign an importance score to each group by using a machine learning method for classification. Finally, we apply a modeling component and obtain outputs. One of the most important outputs of miRdisNET is the performance of miRNA-disease prediction. Compared with the existing methods, miRdisNET obtained the highest AUC value of .9998. Another output of miRdisNET is a list of significant miRNAs for disease under study. The miRNAs identified by miRdisNET are validated via referring to the gold-standard databases which hold information on experimentally verified microRNA-disease associations. miRdisNET has been developed to predict candidate miRNAs for new diseases, where miRNA-disease relation is not yet known. In addition, miRdisNET presents candidate disease-disease associations based on shared miRNA knowledge. The miRdisNET tool and other supplementary files are publicly available at: https://github.com/malikyousef/miRdisNET.

Machine learning in the development of targeting microRNAs in human disease

A microRNA is a small, single-stranded, non-coding ribonucleic acid that plays a crucial role in RNA silencing and can regulate gene expression. With the in-depth study of miRNA in development and disease, miRNA has become an attractive target for novel therapeutic strategies. Exploring miRNA targeting therapy only through experiments is expensive and laborious, so it is essential to develop novel and efficient computational methods to narrow down the search. Recent advances in machine learning applied in biomedical informatics provide opportunities to explore miRNA-targeting drugs, thus promoting miRNA therapeutics. This review provides an overview of recent advancements in miRNA targeting therapeutic using machine learning. First, we mainly describe the basics of predicting miRNA targeting drugs, including pharmacogenomic data resources and data preprocessing. Then we present primary machine learning algorithms and elaborate their application in discovering relationships among miRNAs, drugs, and diseases. Along with the progress of miRNA targeting therapeutics, we finally analyze and discuss the current challenges and opportunities that machine learning confronts.

MicroRNA Regulatory Network Analysis Using miRNet 2.0

MicroRNAs exert their effects in the context of gene regulatory networks. The recent development of high-throughput experimental approaches and the growing availability of gene expression data have permitted comprehensive functional studies of miRNAs. However, the data interpretation is often challenging due to the fact that miRNAs not only act cooperatively with other miRNAs but also participate in complex networks by interacting with other functional elements, including non-coding RNAs or transcription factors that often have extensive effects on cell biology. This chapter provides detailed practical procedures on how to use miRNet 2.0 ( https://www.mirnet.ca ) to perform miRNA regulatory network analytics to gain functional insights.

Identifying SM-miRNA associations based on layer attention graph convolutional network and matrix decomposition

The accurate prediction of potential associations between microRNAs (miRNAs) and small molecule (SM) drugs can enhance our knowledge of how SM cures endogenous miRNA-related diseases. Given that traditional methods for predicting SM-miRNA associations are time-consuming and arduous, a number of computational models have been proposed to anticipate the potential SM-miRNA associations. However, several of these strategies failed to eliminate noise from the known SM-miRNA association information or failed to prioritize the most significant known SM-miRNA associations. Therefore, we proposed a model of Graph Convolutional Network with Layer Attention mechanism for SM-MiRNA Association prediction (GCNLASMMA). Firstly, we obtained the new SM-miRNA associations by matrix decomposition. The new SM-miRNA associations, as well as the integrated SM similarity and miRNA similarity were subsequently incorporated into a heterogeneous network. Finally, a graph convolutional network with an attention mechanism was used to compute the reconstructed SM-miRNA association matrix. Furthermore, four types of cross validations and two types of case studies were performed to assess the performance of GCNLASMMA. In cross validation, global Leave-One-Out Cross Validation (LOOCV), miRNA-fixed LOOCV, SM-fixed LOOCV and 5-fold cross-validation achieved excellent performance. Numerous hypothesized associations in case studies were confirmed by experimental literatures. All of these results confirmed that GCNLASMMA is a trustworthy association inference method.

DAESTB: inferring associations of small molecule-miRNA via a scalable tree boosting model based on deep autoencoder

MicroRNAs (miRNAs) are closely related to a variety of human diseases, not only regulating gene expression, but also having an important role in human life activities and being viable targets of small molecule drugs for disease treatment. Current computational techniques to predict the potential associations between small molecule and miRNA are not that accurate. Here, we proposed a new computational method based on a deep autoencoder and a scalable tree boosting model (DAESTB), to predict associations between small molecule and miRNA. First, we constructed a high-dimensional feature matrix by integrating small molecule-small molecule similarity, miRNA-miRNA similarity and known small molecule-miRNA associations. Second, we reduced feature dimensionality on the integrated matrix using a deep autoencoder to obtain the potential feature representation of each small molecule-miRNA pair. Finally, a scalable tree boosting model is used to predict small molecule and miRNA potential associations. The experiments on two datasets demonstrated the superiority of DAESTB over various state-of-the-art methods. DAESTB achieved the best AUC value. Furthermore, in three case studies, a large number of predicted associations by DAESTB are confirmed with the public accessed literature. We envision that DAESTB could serve as a useful biological model for predicting potential small molecule-miRNA associations.

miR2Trait: an integrated resource for investigating miRNA-disease associations

MicroRNAs are key components of cellular regulatory networks, and breakdown in miRNA function causes cascading effects leading to pathophenotypes. A better understanding of the role of miRNAs in diseases is essential for human health. Here, we have devised a method for comprehensively mapping the associations between miRNAs and diseases by merging on a common key between two curated omics databases. The resulting bidirectional resource, miR2Trait, is more detailed than earlier catalogs, uncovers new relationships, and includes analytical utilities to interrogate and extract knowledge from these datasets. miR2Trait provides resources to compute the disease enrichment of a user-given set of miRNAs and analyze the miRNA profile of a specified diseasome. Reproducible examples demonstrating use-cases for each of these resource components are illustrated. Furthermore we used these tools to construct pairwise miRNA-miRNA and disease-disease enrichment networks, and identified 23 central miRNAs that could underlie major regulatory functions in the human genome. miR2Trait is available as an open-source command-line interface in Python3 (URL: https://github.com/miR2Trait) with a companion wiki documenting the scripts and data resources developed, under MIT license for commercial and non-commercial use. A minimal web-based implementation has been made available at https://sas.sastra.edu/pymir18. Supplementary information is available at: https://doi.org/10.6084/m9.figshare.8288825.v3.

miRNA therapeutics in precision oncology: a natural premium to nurture

The dynamic spectrum of microRNA (miRNA) has grown significantly over the years with its identification and exploration in cancer therapeutics and is currently identified as an important resource for innovative strategies due to its functional behavior for gene regulation and modulation of complex biological networks. The progression of cancer is the consequence of uncontrolled, nonsynchronous procedural faults in the biological system. Diversified and variable cellular response of cancerous cells has always raised challenges in effective cancer therapy. miRNAs, a class of non-coding RNAs (ncRNAs), are the natural genetic gift, responsible to preserve the homeostasis of cell to nurture. The unprecedented significance of endogenous miRNAs has exhibited promising therapeutic potential in cancer therapeutics. Currently, miRNA mimic miR-34, and an antimiR aimed against miR-122 has entered the clinical trials for cancer treatments. This review, highlights the recent breakthroughs, challenges, clinical trials, and advanced delivery vehicles in the administration of miRNA therapies for precision oncology.

MiRLog and dbmiR: prioritization and functional annotation tools to study human microRNA sequence variants

The recent identification of noncoding variants with pathogenic effects suggests that these variations could underlie a significant number of undiagnosed cases. Several computational methods have been developed to predict the functional impact of noncoding variants, but they exhibit only partial concordance and are not integrated with functional annotation resources, making the interpretation of these variants still challenging. MicroRNAs (miRNAs) are small noncoding RNA molecules that act as fine regulators of gene expression and play crucial functions in several biological processes, such as cell proliferation and differentiation. An increasing number of studies demonstrate a significant impact of miRNA single nucleotide variants (SNVs) both in Mendelian diseases and complex traits. To predict the functional effect of miRNA SNVs, we implemented a new meta‐predictor, MiRLog, and we integrated it into a comprehensive database, dbmiR, which includes a precompiled list of all possible miRNA allelic SNVs, providing their biological annotations at nucleotide and miRNA levels. MiRLog and dbmiR were used to explore the genetic variability of miRNAs in 15,708 human genomes included in the gnomAD project, finding several ultra‐rare SNVs with a potentially deleterious effect on miRNA biogenesis and function representing putative contributors to human phenotypes.

miRCom: Tensor Completion Integrating Multi-View Information to Deduce the Potential Disease-Related miRNA-miRNA Pairs

MicroRNAs (miRNAs) are consistently capable of regulating gene expression synergistically in a combination mode and play a key role in various biological processes associated with the initiation and development of human diseases, which indicate that comprehending the synergistic molecular mechanism of miRNAs may facilitate understanding the pathogenesis of diseases or even overcome it. However, most existing computational methods had an incomprehensive acknowledge of the miRNA synergistic effect on the pathogenesis of complex diseases, or were hard to be extended to a large-scale prediction task of miRNA synergistic combinations for different diseases. In this article, we propose a novel tensor completion framework integrating multi-view miRNAs and diseases information, called miRCom, for the discovery of potential disease-associated miRNA-miRNA pairs. We first construct an incomplete three-order association tensor and several types of similarity matrices based on existing biological knowledge. Then, we formulate an objective function via performing the factorizations of coupled tensor and matrices simultaneously. Finally, we build an optimization schema by adopting the ADMM algorithm. After that, we obtain the prediction of miRNA-miRNA pairs for different diseases from the full tensor. The contrastive experimental results with other approaches verified that miRCom effectively identify the potential disease-related miRNA-miRNA pairs. Moreover, case study results further illustrated that miRNA-miRNA pairs have more biologically significance and prognostic value than single miRNAs.

miRModuleNet: Detecting miRNA-mRNA Regulatory Modules

Increasing evidence that microRNAs (miRNAs) play a key role in carcinogenesis has revealed the need for elucidating the mechanisms of miRNA regulation and the roles of miRNAs in gene-regulatory networks. A better understanding of the interactions between miRNAs and their mRNA targets will provide a better understanding of the complex biological processes that occur during carcinogenesis. Increased efforts to reveal these interactions have led to the development of a variety of tools to detect and understand these interactions. We have recently described a machine learning approach miRcorrNet, based on grouping and scoring (ranking) groups of genes, where each group is associated with a miRNA and the group members are genes with expression patterns that are correlated with this specific miRNA. The miRcorrNet tool requires two types of -omics data, miRNA and mRNA expression profiles, as an input file. In this study we describe miRModuleNet, which groups mRNA (genes) that are correlated with each miRNA to form a star shape, which we identify as a miRNA-mRNA regulatory module. A scoring procedure is then applied to each module to further assess their contribution in terms of classification. An important output of miRModuleNet is that it provides a hierarchical list of significant miRNA-mRNA regulatory modules. miRModuleNet was further validated on external datasets for their disease associations, and functional enrichment analysis was also performed. The application of miRModuleNet aids the identification of functional relationships between significant biomarkers and reveals essential pathways involved in cancer pathogenesis. The miRModuleNet tool and all other supplementary files are available at https://github.com/malikyousef/miRModuleNet/

Dissecting miRNA–Gene Networks to Map Clinical Utility Roads of Pharmacogenomics-Guided Therapeutic Decisions in Cardiovascular Precision Medicine

MicroRNAs (miRNAs) create systems networks and gene-expression circuits through molecular signaling and cell interactions that contribute to health imbalance and the emergence of cardiovascular disorders (CVDs). Because the clinical phenotypes of CVD patients present a diversity in their pathophysiology and heterogeneity at the molecular level, it is essential to establish genomic signatures to delineate multifactorial correlations, and to unveil the variability seen in therapeutic intervention outcomes. The clinically validated miRNA biomarkers, along with the relevant SNPs identified, have to be suitably implemented in the clinical setting in order to enhance patient stratification capacity, to contribute to a better understanding of the underlying pathophysiological mechanisms, to guide the selection of innovative therapeutic schemes, and to identify innovative drugs and delivery systems. In this article, the miRNA–gene networks and the genomic signatures resulting from the SNPs will be analyzed as a method of highlighting specific gene-signaling circuits as sources of molecular knowledge which is relevant to CVDs. In concordance with this concept, and as a case study, the design of the clinical trial GESS (NCT03150680) is referenced. The latter is presented in a manner to provide a direction for the improvement of the implementation of pharmacogenomics and precision cardiovascular medicine trials.

Systematic Review: microRNAs as Potential Biomarkers in Mild Cognitive Impairment Diagnosis

The rate of progression from Mild Cognitive Impairment (MCI) to Alzheimer's disease (AD) is estimated at >10% per year, reaching up to 80-90% after 6 years. MCI is considered an indicator of early-stage AD. In this context, the diagnostic screening of MCI is crucial for detecting individuals at high risk of AD before they progress and manifest further severe symptoms. Typically, MCI has been determined using neuropsychological assessment tools such as the Montreal Cognitive Assessment (MoCA) or Mini-Mental Status Examination (MMSE). Unfortunately, other diagnostic methods are not available or are unable to identify MCI in its early stages. Therefore, identifying new biomarkers for MCI diagnosis and prognosis is a significant challenge. In this framework, miRNAs in serum, plasma, and other body fluids have emerged as a promising source of biomarkers for MCI and AD-related cognitive impairments. Interestingly, miRNAs can regulate several signaling pathways via multiple and diverse targets in response to pathophysiological stimuli. This systematic review aims to describe the current state of the art regarding AD-related target genes modulated by differentially expressed miRNAs in peripheral fluids samples in MCI subjects to identify potential miRNA biomarkers in the early stages of AD. We found 30 articles that described five miRNA expression profiles from peripheral fluid in MCI subjects, showing possible candidates for miRNA biomarkers that may be followed up as fluid biomarkers or therapeutic targets of early-stage AD. However, additional research is needed to validate these miRNAs and characterize the precise neuropathological mechanisms.

Potential Molecular Mechanisms and Remdesivir Treatment for Acute Respiratory Syndrome Corona Virus 2 Infection/COVID 19 Through RNA Sequencing and Bioinformatics Analysis

Introduction:

Severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) infections (COVID 19) is a progressive viral infection that has been investigated extensively. However, genetic features and molecular pathogenesis underlying remdesivir treatment for SARS-CoV-2 infection remain unclear. Here, we used bioinformatics to investigate the candidate genes associated in the molecular pathogenesis of remdesivir-treated SARS-CoV-2-infected patients.

Methods:

Expression profiling by high-throughput sequencing dataset (GSE149273) was downloaded from the Gene Expression Omnibus, and the differentially expressed genes (DEGs) in remdesivir-treated SARS-CoV-2 infection samples and nontreated SARS-CoV-2 infection samples with an adjusted P value of <.05 and a |log fold change| > 1.3 were first identified by limma in R software package. Next, pathway and gene ontology (GO) enrichment analysis of these DEGs was performed. Then, the hub genes were identified by the NetworkAnalyzer plugin and the other bioinformatics approaches including protein-protein interaction network analysis, module analysis, target gene—miRNA regulatory network, and target gene—TF regulatory network. Finally, a receiver-operating characteristic analysis was performed for diagnostic values associated with hub genes.

Results:

A total of 909 DEGs were identified, including 453 upregulated genes and 457 downregulated genes. As for the pathway and GO enrichment analysis, the upregulated genes were mainly linked with influenza A and defense response, whereas downregulated genes were mainly linked with drug metabolism—cytochrome P450 and reproductive process. In addition, 10 hub genes (VCAM1, IKBKE, STAT1, IL7R, ISG15, E2F1, ZBTB16, TFAP4, ATP6V1B1, and APBB1) were identified. Receiver-operating characteristic analysis showed that hub genes (CIITA, HSPA6, MYD88, SOCS3, TNFRSF10A, ADH1A, CACNA2D2, DUSP9, FMO5, and PDE1A) had good diagnostic values.

Conclusion:

This study provided insights into the molecular mechanism of remdesivir-treated SARS-CoV-2 infection that might be useful in further investigations.

Identification of miRNA-Small Molecule Associations by Continuous Feature Representation Using Auto-Encoders

MicroRNAs (miRNAs) are short non-coding RNAs that play important roles in the body and affect various diseases, including cancers. Controlling miRNAs with small molecules is studied herein to provide new drug repurposing perspectives for miRNA-related diseases. Experimental methods are time- and effort-consuming, so computational techniques have been applied, relying mostly on biological feature similarities and a network-based scheme to infer new miRNA–small molecule associations. Collecting such features is time-consuming and may be impractical. Here we suggest an alternative method of similarity calculation, representing miRNAs and small molecules through continuous feature representation. This representation is learned by the proposed deep learning auto-encoder architecture. Our suggested representation was compared to previous works and achieved comparable results using 5-fold cross validation (92% identified within top 25% predictions), and better predictions for most of the case studies (avg. of 31% vs. 25% identified within the top 25% of predictions). The results proved the effectiveness of our proposed method to replace previous time- and effort-consuming methods.

Dual-Network Collaborative Matrix Factorization for predicting small molecule-miRNA associations

MicroRNAs (miRNAs) play crucial roles in multiple biological processes and human diseases and can be considered as therapeutic targets of small molecules (SMs). Because biological experiments used to verify SM-miRNA associations are time-consuming and expensive, it is urgent to propose new computational models to predict new SM-miRNA associations. Here, we proposed a novel method called Dual-network Collaborative Matrix Factorization (DCMF) for predicting the potential SM-miRNA associations. Firstly, we utilized the Weighted K Nearest Known Neighbors (WKNKN) method to preprocess SM-miRNA association matrix. Then, we constructed matrix factorization model to obtain two feature matrices containing latent features of SM and miRNA, respectively. Finally, the predicted SM-miRNA association score matrix was obtained by calculating the inner product of two feature matrices. The main innovations of this method were that the use of WKNKN method can preprocess the missing values of association matrix and the introduction of dual network can integrate more diverse similarity information into DCMF. For evaluating the validity of DCMF, we implemented four different cross validations (CVs) based on two distinct datasets and two different case studies. Finally, based on dataset 1 (dataset 2), DCMF achieved Area Under receiver operating characteristic Curves (AUC) of 0.9868 (0.8770), 0.9833 (0.8836), 0.8377 (0.7591) and 0.9836 ± 0.0030 (0.8632 ± 0.0042) in global Leave-One-Out Cross Validation (LOOCV), miRNA-fixed local LOOCV, SM-fixed local LOOCV and 5-fold CV, respectively. For case studies, plenty of predicted associations have been confirmed by published experimental literature. Therefore, DCMF is an effective tool to predict potential SM-miRNA associations.

Ensemble of kernel ridge regression-based small molecule-miRNA association prediction in human disease

MicroRNAs (miRNAs) play crucial roles in human disease and can be targeted by small molecule (SM) drugs according to numerous studies, which shows that identifying SM-miRNA associations in human disease is important for drug development and disease treatment. We proposed the method of Ensemble of Kernel Ridge Regression-based Small Molecule-MiRNA Association prediction (EKRRSMMA) to uncover potential SM-miRNA associations by combing feature dimensionality reduction and ensemble learning. First, we constructed different feature subsets for both SMs and miRNAs. Then, we trained homogeneous base learners based on distinct feature subsets and took the average of scores obtained from these base learners as SM-miRNA association score. In EKRRSMMA, feature dimensionality reduction technology was employed in the process of construction of feature subsets to reduce the influence of noisy data. Besides, the base learner, namely KRR_avg, was the combination of two classifiers constructed under SM space and miRNA space, which could make full use of the information of SM and miRNA. To assess the prediction performance of EKRRSMMA, we conducted Leave-One-Out Cross-Validation (LOOCV), SM-fixed local LOOCV, miRNA-fixed local LOOCV and 5-fold CV based on two datasets. For Dataset 1 (Dataset 2), EKRRSMMA got the Area Under receiver operating characteristic Curves (AUCs) of 0.9793 (0.8871), 0.8071 (0.7705), 0.9732 (0.8586) and 0.9767 ± 0.0014 (0.8560 ± 0.0027). Besides, we conducted four case studies. As a result, 32 (5-Fluorouracil), 19 (17β-Estradiol), 26 (5-Aza-2'-deoxycytidine) and 11 (cyclophosphamide) out of top 50 predicted potentially associated miRNAs were confirmed by database or experimental literature. Above evaluation results demonstrated that EKRRSMMA is reliable for predicting SM-miRNA associations.

Integration of pairwise neighbor topologies and miRNA family and cluster attributes for miRNA-disease association prediction

Identifying disease-related microRNAs (miRNAs) assists the understanding of disease pathogenesis. Existing research methods integrate multiple kinds of data related to miRNAs and diseases to infer candidate disease-related miRNAs. The attributes of miRNA nodes including their family and cluster belonging information, however, have not been deeply integrated. Besides, the learning of neighbor topology representation of a pair of miRNA and disease is a challenging issue. We present a disease-related miRNA prediction method by encoding and integrating multiple representations of miRNA and disease nodes learnt from the generative and adversarial perspective. We firstly construct a bilayer heterogeneous network of miRNA and disease nodes, and it contains multiple types of connections among these nodes, which reflect neighbor topology of miRNA-disease pairs, and the attributes of miRNA nodes, especially miRNA-related families and clusters. To learn enhanced pairwise neighbor topology, we propose a generative and adversarial model with a convolutional autoencoder-based generator to encode the low-dimensional topological representation of the miRNA-disease pair and multi-layer convolutional neural network-based discriminator to discriminate between the true and false neighbor topology embeddings. Besides, we design a novel feature category-level attention mechanism to learn the various importance of different features for final adaptive fusion and prediction. Comparison results with five miRNA-disease association methods demonstrated the superior performance of our model and technical contributions in terms of area under the receiver operating characteristic curve and area under the precision-recall curve. The results of recall rates confirmed that our model can find more actual miRNA-disease associations among top-ranked candidates. Case studies on three cancers further proved the ability to detect potential candidate miRNAs.

A comprehensive review of online resources for microRNA-diseases associations: the state of the art

MicroRNAs (miRNAs) as small 19- to 24-nucleotide noncoding RNAs regulate several mRNA targets and signaling pathways. Therefore, miRNAs are considered key regulators in cellular pathways as well as various pathologies. There is substantial interest in the relationship between disease and miRNAs, which made that one of the important research topics. Interestingly, miRNAs emerged as an attractive approach for clinical application, not only as biomarkers for diagnosis and prognosis or in the prediction of therapy response but also as therapeutic tools. For these purposes, the identification of crucial miRNAs in disease is very important. Databases provided valuable experimental and computational miRNAs-disease information in an accessible and comprehensive manner, such as miRNA target genes, miRNA related in signaling pathways and miRNA involvement in various diseases. In this review, we summarized miRNAs-disease databases in two main categories based on the general or specific diseases. In these databases, researchers could search diseases to identify critical miRNAs and developed that for clinical applications. In another way, by searching particular miRNAs, they could recognize in which disease these miRNAs would be dysregulated. Despite the significant development that has been done in these databases, there are still some limitations, such as not being updated and not providing uniform and detailed information that should be resolved in future databases. This survey can be helpful as a comprehensive reference for choosing a suitable database by researchers and as a guideline for comparing the features and limitations of the database by developer or designer. Short abstract We summarized miRNAs-disease databases that researchers could search disease to identify critical miRNAs and developed that for clinical applications. This survey can help choose a suitable database for researchers.

MicroRNAs and nervous system diseases: network insights and computational challenges

The nervous system is one of the most complex biological systems, and nervous system disease (NSD) is a major cause of disability and mortality. Extensive evidence indicates that numerous dysregulated microRNAs (miRNAs) are involved in a broad spectrum of NSDs. A comprehensive review of miRNA-mediated regulatory will facilitate our understanding of miRNA dysregulation mechanisms in NSDs. In this work, we summarized currently available databases on miRNAs and NSDs, star NSD miRNAs, NSD spectrum width, miRNA spectrum width and the distribution of miRNAs in NSD sub-categories by reviewing approximately 1000 studies. In addition, we characterized miRNA-miRNA and NSD-NSD interactions from a network perspective based on miRNA-NSD benchmarking data sets. Furthermore, we summarized the regulatory principles of miRNAs in NSDs, including miRNA synergistic regulation in NSDs, miRNA modules and NSD modules. We also discussed computational challenges for identifying novel miRNAs in NSDs. Elucidating the roles of miRNAs in NSDs from a network perspective would not only improve our understanding of the precise mechanism underlying these complex diseases, but also provide novel insight into the development, diagnosis and treatment of NSDs.

Predicting potential small molecule-miRNA associations based on bounded nuclear norm regularization

Mounting evidence has demonstrated the significance of taking microRNAs (miRNAs) as the target of small molecule (SM) drugs for disease treatment. Given the fact that exploring new SM-miRNA associations through biological experiments is extremely expensive, several computing models have been constructed to reveal the possible SM-miRNA associations. Here, we built a computing model of Bounded Nuclear Norm Regularization for SM-miRNA Associations prediction (BNNRSMMA). Specifically, we first constructed a heterogeneous SM-miRNA network utilizing miRNA similarity, SM similarity, confirmed SM-miRNA associations and defined a matrix to represent the heterogeneous network. Then, we constructed a model to complete this matrix by minimizing its nuclear norm. The Alternating Direction Method of Multipliers was adopted to minimize the nuclear norm and obtain predicted scores. The main innovation lies in two aspects. During completion, we limited all elements of the matrix within the interval of (0,1) to make sure they have practical significance. Besides, instead of strictly fitting all known elements, a regularization term was incorporated to tolerate the noise in integrated similarities. Furthermore, four kinds of cross-validations on two datasets and two types of case studies were performed to evaluate the predictive performance of BNNRSMMA. Finally, BNNRSMMA attained areas under the curve of 0.9822 (0.8433), 0.9793 (0.8852), 0.8253 (0.7350) and 0.9758 ± 0.0029 (0.8759 ± 0.0041) under global leave-one-out cross-validation (LOOCV), miRNA-fixed LOOCV, SM-fixed LOOCV and 5-fold cross-validation based on Dataset 1(Dataset 2), respectively. With regard to case studies, plenty of predicted associations have been verified by experimental literatures. All these results confirmed that BNNRSMMA is a reliable tool for inferring associations.

RSCMDA: Prediction of Potential miRNA-Disease Associations Based on a Robust Similarity Constraint Learning Method

With the rapid development of biotechnology and computer technology, increasing studies have shown that the occurrence of many diseases in the human body is closely related to the dysfunction of miRNA, and the relationship between them has become a new research hotspot. Exploring disease-related miRNAs information provides a new perspective for understanding the etiology and pathogenesis of diseases. In this study, we proposed a new method based on similarity constrained learning (RSCMDA) to infer disease-associated miRNAs. Considering the problems of noise and incomplete information in current biological datasets, we designed a new framework RSCMDA, which can learn a new disease similarity network and miRNA similarity network based on the existing biological information, and then update the predicted miRNA-disease associations using robust similarity constraint learning method. Consequently, the AUC scores obtained in the global and local cross-validation of RSCMDA are 0.9465 and 0.8494, respectively, which are superior to the other methods. Besides, the prediction performance of RSCMDA is further confirmed by the case study on lung Neoplasms, because 94% of the top 50 miRNAs predicted by the RSCMDA method are confirmed from the existing biological databases or research results. All the results show that RSCMDA is a reliable and effective framework, which can be used as new technology to explore the relationship between miRNA and disease.

Structure-aware machine learning identifies microRNAs operating as Toll-like receptor 7/8 ligands

MicroRNAs (miRNAs) can serve as activation signals for membrane receptors, a recently discovered function that is independent of the miRNAs’ conventional role in post-transcriptional gene regulation. Here, we introduce a machine learning approach, BrainDead, to identify oligonucleotides that act as ligands for single-stranded RNA-detecting Toll-like receptors (TLR)7/8, thereby triggering an immune response. BrainDead was trained on activation data obtained from in vitro experiments on murine microglia, incorporating sequence and intra-molecular structure, as well as inter-molecular homo-dimerization potential of candidate RNAs. The method was applied to analyse all known human miRNAs regarding their potential to induce TLR7/8 signalling and microglia activation. We validated the predicted functional activity of subsets of high- and low-scoring miRNAs experimentally, of which a selection has been linked to Alzheimer’s disease. High agreement between predictions and experiments confirms the robustness and power of BrainDead. The results provide new insight into the mechanisms of how miRNAs act as TLR ligands. Eventually, BrainDead implements a generic machine learning methodology for learning and predicting the functions of short RNAs in any context.

Bioinformatics Accelerates the Major Tetrad: A Real Boost for the Pharmaceutical Industry

With advanced technology and its development, bioinformatics is one of the avant-garde fields that has managed to make amazing progress in the pharmaceutical-medical field by modeling the infrastructural dimensions of healthcare and integrating computing tools in drug innovation, facilitating prevention, detection/more accurate diagnosis, and treatment of disorders, while saving time and money. By association, bioinformatics and pharmacovigilance promoted both sample analyzes and interpretation of drug side effects, also focusing on drug discovery and development (DDD), in which systems biology, a personalized approach, and drug repositioning were considered together with translational medicine. The role of bioinformatics has been highlighted in DDD, proteomics, genetics, modeling, miRNA discovery and assessment, and clinical genome sequencing. The authors have collated significant data from the most known online databases and publishers, also narrowing the diversified applications, in order to target four major areas (tetrad): DDD, anti-microbial research, genomic sequencing, and miRNA research and its significance in the management of current pandemic context. Our analysis aims to provide optimal data in the field by stratification of the information related to the published data in key sectors and to capture the attention of researchers interested in bioinformatics, a field that has succeeded in advancing the healthcare paradigm by introducing developing techniques and multiple database platforms, addressed in the manuscript.

emiRIT: a text-mining-based resource for microRNA information

microRNAs (miRNAs) are essential gene regulators, and their dysregulation often leads to diseases. Easy access to miRNA information is crucial for interpreting generated experimental data, connecting facts across publications and developing new hypotheses built on previous knowledge. Here, we present extracting miRNA Information from Text (emiRIT), a text-miningbased resource, which presents miRNA information mined from the literature through a user-friendly interface. We collected 149 ,233 miRNA –PubMed ID pairs from Medline between January 1997 and May 2020. emiRIT currently contains ‘miRNA –gene regulation’ (69 ,152 relations), ‘miRNA disease (cancer)’ (12 ,300 relations), ‘miRNA –biological process and pathways’ (23, 390 relations) and circulatory ‘miRNAs in extracellular locations’ (3782 relations). Biological entities and their relation to miRNAs were extracted from Medline abstracts using publicly available and in-house developed text-mining tools, and the entities were normalized to facilitate querying and integration. We built a database and an interface to store and access the integrated data, respectively. We provide an up-to-date and user-friendly resource to facilitate access to comprehensive miRNA information from the literature on a large scale, enabling users to navigate through different roles of miRNA and examine them in a context specific to their information needs. To assess our resource’s information coverage, we have conducted two case studies focusing on the target and differential expression information of miRNAs in the context of cancer and a third case study to assess the usage of emiRIT in the curation of miRNA information.

Database URL: https://research.bioinformatics.udel.edu/emirit/

Micro-RNA Quantification, Target Gene Identification, and Pathway Analysis

RNA sequencing has become a powerful tool for profiling the expression level of small RNAs from both solid tissues and liquid biopsies. In conjunction with pathway analysis, it offers exciting possibilities for the identification of disease specific biomarkers. In this chapter, we describe a workflow for processing this type of sequencing data. We start by removing technical sequences (adapters) and by performing quality control, a critical task that is necessary to identify possible issues caused by sample preparation and library sequencing. We then describe read alignment and gene-level abundance estimation. Building on these results, we normalize expression profiles and compute differentially expressed microRNAs between sample groups of interest. We conclude by showing how to employ pathway analysis to identify molecular signatures corresponding to biological processes that are significantly altered by the action for microRNAs.

Combined embedding model for MiRNA-disease association prediction

BACKGROUND

Cumulative evidence from biological experiments has confirmed that miRNAs have significant roles to diagnose and treat complex diseases. However, traditional medical experiments have limitations in time-consuming and high cost so that they fail to find the unconfirmed miRNA and disease interactions. Thus, discovering potential miRNA-disease associations will make a contribution to the decrease of the pathogenesis of diseases and benefit disease therapy. Although, existing methods using different computational algorithms have favorable performances to search for the potential miRNA-disease interactions. We still need to do some work to improve experimental results.

RESULTS

We present a novel combined embedding model to predict MiRNA-disease associations (CEMDA) in this article. The combined embedding information of miRNA and disease is composed of pair embedding and node embedding. Compared with the previous heterogeneous network methods that are merely node-centric to simply compute the similarity of miRNA and disease, our method fuses pair embedding to pay more attention to capturing the features behind the relative information, which models the fine-grained pairwise relationship better than the previous case when each node only has a single embedding. First, we construct the heterogeneous network from supported miRNA-disease pairs, disease semantic similarity and miRNA functional similarity. Given by the above heterogeneous network, we find all the associated context paths of each confirmed miRNA and disease. Meta-paths are linked by nodes and then input to the gate recurrent unit (GRU) to directly learn more accurate similarity measures between miRNA and disease. Here, the multi-head attention mechanism is used to weight the hidden state of each meta-path, and the similarity information transmission mechanism in a meta-path of miRNA and disease is obtained through multiple network layers. Second, pair embedding of miRNA and disease is fed to the multi-layer perceptron (MLP), which focuses on more important segments in pairwise relationship. Finally, we combine meta-path based node embedding and pair embedding with the cost function to learn and predict miRNA-disease association. The source code and data sets that verify the results of our research are shown at https://github.com/liubailong/CEMDA .

CONCLUSIONS

The performance of CEMDA in the leave-one-out cross validation and fivefold cross validation are 93.16% and 92.03%, respectively. It denotes that compared with other methods, CEMDA accomplishes superior performance. Three cases with lung cancers, breast cancers, prostate cancers and pancreatic cancers show that 48,50,50 and 50 out of the top 50 miRNAs, which are confirmed in HDMM V2.0. Thus, this further identifies the feasibility and effectiveness of our method.

MNDR v3.0: mammal ncRNA-disease repository with increased coverage and annotation

Many studies have indicated that non-coding RNA (ncRNA) dysfunction is closely related to numerous diseases. Recently, accumulated ncRNA-disease associations have made related databases insufficient to meet the demands of biomedical research. The constant updating of ncRNA-disease resources has become essential. Here, we have updated the mammal ncRNA-disease repository (MNDR, http://www.rna-society.org/mndr/) to version 3.0, containing more than one million entries, four-fold increment in data compared to the previous version. Experimental and predicted circRNA-disease associations have been integrated, increasing the number of categories of ncRNAs to five, and the number of mammalian species to 11. Moreover, ncRNA-disease related drug annotations and associations, as well as ncRNA subcellular localizations and interactions, were added. In addition, three ncRNA-disease (miRNA/lncRNA/circRNA) prediction tools were provided, and the website was also optimized, making it more practical and user-friendly. In summary, MNDR v3.0 will be a valuable resource for the investigation of disease mechanisms and clinical treatment strategies.

Biomarker Identification for Liver Hepatocellular Carcinoma and Cholangiocarcinoma Based on Gene Regulatory Network Analysis

Background:

Liver hepatocellular carcinoma (LIHC) and cholangiocarcinoma (CHOL) are two main histological subtypes of primary liver cancer with a unified molecular landscape, and feed-forward loops (FFLs) have been shown to be relevant in these complex diseases.

Objective:

To date, there has been no comparative analysis of the pathogenesis of LIHC and CHOL based on regulatory relationships. Therefore, we investigated the common and distinct regulatory properties of LIHC and CHOL in terms of gene regulatory networks.

Method:

Based on identified FFLs and an analysis of pathway enrichment, we constructed pathway-specific co-expression networks and further predicted biomarkers for these cancers by network clustering.

Resul:

We identified 20 and 36 candidate genes for LIHC and CHOL, respectively. The literature from PubMed supports the reliability of our results.

Conclusion:

Our results indicated that the hsa01522-Endocrine resistance pathway was associated with both LIHC and CHOL. Additionally, six genes (SPARC, CTHRC1, COL4A1, EDIL3, LAMA4 and OLFML2B) were predicted to be highly associated with both cancers, and COL4A2, CSPG4, GJC1 and ADAMTS7 were predicted to be potential biomarkers of LIHC, and COL6A3, COL1A2, FAP and COL8A1 were predicted to be potential biomarkers of CHOL. In addition, we inferred that the Collagen gene family, which appeared more frequently in our overall prediction results, might be closely related to cancer development.

A comprehensive survey on computational methods of non-coding RNA and disease association prediction

The studies on relationships between non-coding RNAs and diseases are widely carried out in recent years. A large number of experimental methods and technologies of producing biological data have also been developed. However, due to their high labor cost and production time, nowadays, calculation-based methods, especially machine learning and deep learning methods, have received a lot of attention and been used commonly to solve these problems. From a computational point of view, this survey mainly introduces three common non-coding RNAs, i.e. miRNAs, lncRNAs and circRNAs, and the related computational methods for predicting their association with diseases. First, the mainstream databases of above three non-coding RNAs are introduced in detail. Then, we present several methods for RNA similarity and disease similarity calculations. Later, we investigate ncRNA-disease prediction methods in details and classify these methods into five types: network propagating, recommend system, matrix completion, machine learning and deep learning. Furthermore, we provide a summary of the applications of these five types of computational methods in predicting the associations between diseases and miRNAs, lncRNAs and circRNAs, respectively. Finally, the advantages and limitations of various methods are identified, and future researches and challenges are also discussed.

Bioinformatics analyses of significant genes, related pathways, and candidate diagnostic biomarkers and molecular targets in SARS-CoV-2/COVID-19

Severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) infection is a leading cause of pneumonia and death. The aim of this investigation is to identify the key genes in SARS-CoV-2 infection and uncover their potential functions. We downloaded the expression profiling by high throughput sequencing of GSE152075 from the Gene Expression Omnibus database. Normalization of the data from primary SARS-CoV-2 infected samples and negative control samples in the database was conducted using R software. Then, joint analysis of the data was performed. Pathway and Gene ontology (GO) enrichment analyses were performed, and the protein-protein interaction (PPI) network, target gene - miRNA regulatory network, target gene - TF regulatory network of the differentially expressed genes (DEGs) were constructed using Cytoscape software. Identification of diagnostic biomarkers was conducted using receiver operating characteristic (ROC) curve analysis. 994 DEGs (496 up regulated and 498 down regulated genes) were identified. Pathway and GO enrichment analysis showed up and down regulated genes mainly enriched in the NOD-like receptor signaling pathway, Ribosome, response to external biotic stimulus and viral transcription in SARS-CoV-2 infection. Down and up regulated genes were selected to establish the PPI network, modules, target gene - miRNA regulatory network, target gene - TF regulatory network revealed that these genes were involved in adaptive immune system, fluid shear stress and atherosclerosis, influenza A and protein processing in endoplasmic reticulum. In total, ten genes (CBL, ISG15, NEDD4, PML, REL, CTNNB1, ERBB2, JUN, RPS8 and STUB1) were identified as good diagnostic biomarkers. In conclusion, the identified DEGs, hub genes and target genes contribute to the understanding of the molecular mechanisms underlying the advancement of SARS-CoV-2 infection and they may be used as diagnostic and molecular targets for the treatment of patients with SARS-CoV-2 infection in the future.