FMSM: a novel computational model for predicting potential miRNA biomarkers for various human diseases

Impact of smoking-induced dysregulated human miRNAs in chronic disease development and their potential use in prognostic and therapeutic purposes

MicroRNAs (miRNAs) are evolutionary conserved small noncoding RNA molecules with a significant ability to regulate gene expression at the posttranscriptional level either through translation repression or messenger RNA degradation. miRNAs are differentially expressed in various pathophysiological conditions, affecting the course of the disease by modulating several critical target genes. As the persistence of irreversible molecular changes caused by cigarette smoking is central to the pathogenesis of various chronic diseases, several studies have shown its direct correlation with the dysregulation of different miRNAs, affecting numerous essential biological processes. This review provides an insight into the current status of smoking-induced miRNAs dysregulation in chronic diseases such as COPD, atherosclerosis, pulmonary hypertension, and different cancers and explores the diagnostic/prognostic potential of miRNA-based biomarkers and their efficacy as therapeutic targets.

GBDR: a Bayesian model for precise prediction of pathogenic microorganisms using 16S rRNA gene sequences

BACKGROUND

Recent evidences have suggested that human microorganisms participate in important biological activities in the human body. The dysfunction of host-microbiota interactions could lead to complex human disorders. The knowledge on host-microbiota interactions can provide valuable insights into understanding the pathological mechanism of diseases. However, it is time-consuming and costly to identify the disorder-specific microbes from the biological "haystack" merely by routine wet-lab experiments. With the developments in next-generation sequencing and omics-based trials, it is imperative to develop computational prediction models for predicting microbe-disease associations on a large scale.

RESULTS

Based on the known microbe-disease associations derived from the Human Microbe-Disease Association Database (HMDAD), the proposed model shows reliable performance with high values of the area under ROC curve (AUC) of 0.9456 and 0.8866 in leave-one-out cross validations and five-fold cross validations, respectively. In case studies of colorectal carcinoma, 80% out of the top-20 predicted microbes have been experimentally confirmed via published literatures.

CONCLUSION

Based on the assumption that functionally similar microbes tend to share the similar interaction patterns with human diseases, we here propose a group based computational model of Bayesian disease-oriented ranking to prioritize the most potential microbes associating with various human diseases. Based on the sequence information of genes, two computational approaches (BLAST+ and MEGA 7) are leveraged to measure the microbe-microbe similarity from different perspectives. The disease-disease similarity is calculated by capturing the hierarchy information from the Medical Subject Headings (MeSH) data. The experimental results illustrate the accuracy and effectiveness of the proposed model. This work is expected to facilitate the characterization and identification of promising microbial biomarkers.

MDA-CF: Predicting MiRNA-Disease associations based on a cascade forest model by fusing multi-source information

MicroRNAs (miRNAs) are significant regulators in various biological processes. They may become promising biomarkers or therapeutic targets, which provide a new perspective in diagnosis and treatment of multiple diseases. Since the experimental methods are always costly and resource-consuming, prediction of disease-related miRNAs using computational methods is in great need. In this study, we developed MDA-CF to identify underlying miRNA-disease associations based on a cascade forest model. In this method, multi-source information was integrated to represent miRNAs and diseases comprehensively, and the autoencoder was utilized for dimension reduction to obtain the optimal feature space. The cascade forest model was then employed for miRNA-disease association prediction. As a result, the average AUC of MDA-CF was 0.9464 on HMDD v3.2 in five-fold cross-validation. Compared with previous computational methods, MDA-CF performed better on HMDD v2.0 with an average AUC of 0.9258. Moreover, MDA-CF was implemented to investigate colon neoplasm, breast neoplasm, and gastric neoplasm, and 100%, 86%, 88% of the top 50 potential miRNAs were validated by authoritative databases. In conclusion, MDA-CF appears to be a reliable method to uncover disease-associated miRNAs. The source code of MDA-CF is available at https://github.com/a1622108/MDA-CF.

Experimental validation of a predicted microRNA within human FVIII gene

Hemophilia A is an X-linked bleeding disorder that occurs due to the deficiency of Factor VIII (FVIII) protein clotting activity. The mutations in the F8 gene, which encodes FVIII coagulating protein have been widely reviewed. However, there is a wide range of criteria that in addition to F8 gene mutations, different molecular mechanisms may be associated with hemophilia A. Various functions of FVIII could be related to the hypothetical small non-coding RNAs, located within the F8 gene sequence. Therefore, miRNAs that can post-transcriptionally regulate gene expression might confer susceptibility to developing hemophilia A. Here, we have selected a bioinformatically predicted hairpin structure sequence in the first intron of the F8 gene that has the potential to produce a real miRNA (named put-miR1). We tried to experimentally detect the predicted miRNA via RT-PCR following its precursor overexpression in HEK 293 cell lines. Despite the accuracy of miRNA prediction, according to the reliable bioinformatics studies, we couldn't confirm the existence of considered mature miRNA in transfected cells. We hope that through changing experimental conditions, designing new primers, or altering cell lines and expression vectors, the exogenous and endogenous expression of the predicted miRNA will be confirmed.

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

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

Focus on MicroRNAs as Biomarker in Pediatric Diseases

BACKGROUND

MiRNAs are a class of small non-coding RNAs that are involved in the post-transcriptional regulation of gene expression. MiRNAs are considered a class of epigenetic biomarkers. These biomarkers can investigate disease at different stages: diagnosis, therapy or clinical follow-up.

OBJECTIVE

The aim of this paper is to highlight the innovative use of miRNAs in several childhood diseases.

METHODS

We conducted a literature review to search the usage of miRNAs in pediatric clinical routine or experimental trials.

RESULTS

We found a possible key role of miRNAs in different pediatric illnesses (metabolic alterations, coagulation defects, cancer).

CONCLUSION

The modest literature production denotes that further investigation is needed to assess and validate the promising role of miRNAs as non-invasive biomarkers in pediatric disorders.

A Novel Approach Based on Bipartite Network Recommendation and KATZ Model to Predict Potential Micro-Disease Associations

Accumulating evidence indicates that the microbes colonizing human bodies have crucial effects on human health and the discovery of disease-related microbes will promote the discovery of biomarkers and drugs for the prevention, diagnosis, treatment, and prognosis of diseases. However clinical experiments of disease-microbe associations are time-consuming, laborious and expensive, and there are few methods for predicting potential microbe-disease association. Therefore, developing effective computational models utilizing the accumulated public data of clinically validated microbe-disease associations to identify novel disease-microbe associations is of practical importance. We propose a novel method based on the KATZ model and Bipartite Network Recommendation Algorithm (KATZBNRA) to discover potential associations between microbes and diseases. We calculate the Gaussian interaction profile kernel similarity of diseases and microbes based on validated disease-microbe associations. Then, we construct a bipartite graph and execute a bipartite network recommendation algorithm. Finally, we integrate the disease similarity, microbe similarity and bipartite network recommendation score to obtain the final score, which is used to infer whether there are some novel disease-microbe interactions. To evaluate the predictive power of KATZBNRA, we tested it with the walk length 2 using global leave-one-out cross validation (LOOV), two-fold and five-fold cross validations, with AUCs of 0.9098, 0.8463 and 0.8969, respectively. The test results also show that KATZBNRA is more accurate than two recent similar methods KATZHMDA and BNPMDA.

Predicting lncRNA-miRNA Interaction via Graph Convolution Auto-Encoder

The interaction of miRNA and lncRNA is known to be important for gene regulations. However, the number of known lncRNA-miRNA interactions is still very limited and there are limited computational tools available for predicting new ones. Considering that lncRNAs and miRNAs share internal patterns in the partnership between each other, the underlying lncRNA-miRNA interactions could be predicted by utilizing the known ones, which could be considered as a semi-supervised learning problem. It is shown that the attributes of lncRNA and miRNA have a close relationship with the interaction between each other. Effective use of side information could be helpful for improving the performance especially when the training samples are limited. In view of this, we proposed an end-to-end prediction model called GCLMI (Graph Convolution for novel lncRNA-miRNA Interactions) by combining the techniques of graph convolution and auto-encoder. Without any preprocessing process on the feature information, our method can incorporate raw data of node attributes with the topology of the interaction network. Based on a real dataset collected from a public database, the results of experiments conducted on k-fold cross validations illustrate the robustness and effectiveness of the prediction performance of the proposed prediction model. We prove the graph convolution layer as designed in the proposed model able to effectively integrate the input data by filtering the graph with node features. The proposed model is anticipated to yield highly potential lncRNA-miRNA interactions in the scenario that different types of numerical features describing lncRNA or miRNA are provided by users, serving as a useful computational tool.