BACKGROUND
Small, non-coding microRNAs, usually of 20-25 nucleotides, are known to regulate the post-transcriptional gene expression, which has a significant role in human biological processes, including immune-biogenesis, homeostasis and infection control as differential expression of such miRNAs is responsible for fine-tuning the organismic development.


METHODS
A search of bibliographic databases was carried out with a focused question on microRNA-Disease Prediction. A deductive qualitative content analysis approach was further employed to assess the research's overall outcomes, review articles on prediction tools in miRNA-Diseases, and analyse the interventions.


RESULTS
Diagnosis and therapeutics of diseases and miRNA prediction methods hold importance in identifying the regulatory mechanisms. Collections of efficient miRNA prediction methods to identify miRNA-mRNA-disease regulatory relationships have been presented through this review, consolidating the potential of miRNAs as a diagnostic and prognostic biomarker of multiple diseases, including COVID-19.


CONCLUSIONS
The role of miRNA in the aetiology and pathogenesis of wide-range of pathologies, including viral, bacterial to chronic diseases such as cancer, is quite feasible through the modern tools in bioinformatics which has been elaborated focusing upon miRNA-disease prediction methods and their application potential establishing miRNAs as a robust and reliable biomarker in clinico-medical studies.

Prediction models based on miRNA-disease relationship: Diagnostic relevance to multiple diseases including COVID-19.

This study's paper examines using a generative opposed network as an excellent way to predict the malignancy of tumors in a clinically applicable manner. The examination outcomes imply that the DCGAN-based total version can make surprisingly dependable predictions of tumor malignancy compared to other machine-mastering strategies. Furthermore, the authors additionally propose that the DCGAN-based total version may be hired in scientific applications with promising effects.

Examining the Use of Generative Adversarial Network for Predicting Tumor Malignancy

Numerous researches emphasized that long non-coding RNA (lncRNA) plays a vital factor in various biological processes, and its mismatched expression and dysfunction are tightly linked with the occurrence of human diseases. Thus, computational models were designed to identify lncRNA-disease interactions by merging heterogeneous biological data. However, most of them neglected the intrinsic structure of multi-source information, which limits the performance for potential lncRNA-disease association prediction. Here, GCN-NIMC is introduced to alleviate the dilemma for disease-associated lncRNA genes identification based on the graph convolutional network with neural inductive matrix. This method builds a feature matrix with multi-source heterogeneous data and then learn the various information contained in the feature matrix for the sake of acquiring better feature expressions of the lncRNA-disease interactions. Experimental results on 10-repeated 5-fold cross-validation demonstrated that our proposed GCN-NIMC is superior to existing cutting-edge methods for identifying disease-related lncRNA genes. Furthermore, case studies confirmed our computational method as a practical tool with clinical benefits to develop the therapeutic schedule at lncRNA-level.

Graph Convolutional Network with Neural Inductive Matrix Completion for Predicting Disease-Related LncRNA Genes

Substantial evidence has shown that microRNAs are crucial for biological processes within complex human diseases. Identifying the association of miRNA–disease pairs will contribute to accelerating the discovery of potential biomarkers and pathogenesis. Researchers began to focus on constructing computational models to facilitate the progress of disease pathology and clinical medicine by identifying the potential disease-related miRNAs. However, most existing computational methods are expensive, and their use is limited to unobserved relationships for unknown miRNAs (diseases) without association information. In this manuscript, we proposed a creatively semi-supervised model named bidirectional generative adversarial network for miRNA-disease association prediction (BGANMDA). First, we constructed a microRNA similarity network, a disease similarity network, and Gaussian interaction profile kernel similarity based on the known miRNA–disease association and comprehensive similarity of miRNAs (diseases). Next, an integrated similarity feature network with the full underlying relationships of miRNA–disease pairwise was obtained. Then, the similarity feature network was fed into the BGANMDA model to learn advanced traits in latent space. Finally, we ranked an association score list and predicted the associations between miRNA and disease. In our experiment, a five-fold cross validation was applied to estimate BGANMDA’s performance, and an area under the curve (AUC) of 0.9319 and a standard deviation of 0.00021 were obtained. At the same time, in the global and local leave-one-out cross validation (LOOCV), the AUC value and standard deviation of BGANMDA were 0.9116 ± 0.0025 and 0.8928 ± 0.0022, respectively. Furthermore, BGANMDA was employed in three different case studies to validate its prediction capability and accuracy. The experimental results of the case studies showed that 46, 46, and 48 of the top 50 prediction lists had been identified in previous studies.

Application of Bidirectional Generative Adversarial Networks to Predict Potential miRNAs Associated With Diseases

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Application of Bidirectional Generative Adversarial Networks to Predict Potential miRNAs Associated With Diseases