Prediction of miRNA targets by learning from interaction sequences.
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ABSTRACT:
SUBMITTER: Zheng X
PROVIDER: S-EPMC7199961 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Long non-coding RNA (LncRNA) and microRNA (miRNA) are both non-coding RNAs that play significant regulatory roles in many life processes. There is cumulating evidence showing that the interaction patterns between lncRNAs and miRNAs are highly related to cancer development, gene regulation, cellular metabolic process, etc. Contemporaneously, with the rapid development of RNA sequence technology, numerous novel lncRNAs and miRNAs have been found, which might help to explore novel regulated patterns. However, the increasing unknown interactions between lncRNAs and miRNAs may hinder finding the novel regulated pattern, and wet experiments to identify the potential interaction are costly and time-consuming. Furthermore, few computational tools are available for predicting lncRNA-miRNA interaction based on a sequential level. In this paper, we propose a hybrid sequence feature-based model, LncMirNet (lncRNA-miRNA interactions network), to predict lncRNA-miRNA interactions via deep convolutional neural networks (CNN). First, four categories of sequence-based features are introduced to encode lncRNA/miRNA sequences including k-mer (k = 1, 2, 3, 4), composition transition distribution (CTD), doc2vec, and graph embedding features. Then, to fit the CNN learning pattern, a histogram-dd method is incorporated to fuse multiple types of features into a matrix. Finally, LncMirNet attained excellent performance in comparison with six other state-of-the-art methods on a real dataset collected from lncRNASNP2 via five-fold cross validation. LncMirNet increased accuracy and area under curve (AUC) by more than 3%, respectively, over that of the other tools, and improved the Matthews correlation coefficient (MCC) by more than 6%. These results show that LncMirNet can obtain high confidence in predicting potential interactions between lncRNAs and miRNAs.
| S-EPMC7583909 | biostudies-literature
Project description:MicroRNAs (miRNAs) are a class of short, non-coding RNA that play regulatory roles in a wide variety of biological processes, such as plant growth and abiotic stress responses. Although several computational tools have been developed to identify primary miRNAs and precursor miRNAs (pre-miRNAs), very few provide the functionality of locating mature miRNAs within plant pre-miRNAs. This manuscript introduces a novel algorithm for predicting miRNAs named miRLocator, which is based on machine learning techniques and sequence and structural features extracted from miRNA:miRNA* duplexes. To address the class imbalance problem (few real miRNAs and a large number of pseudo miRNAs), the prediction models in miRLocator were optimized by considering critical (and often ignored) factors that can markedly affect the prediction accuracy of mature miRNAs, including the machine learning algorithm and the ratio between training positive and negative samples. Ten-fold cross-validation on 5854 experimentally validated miRNAs from 19 plant species showed that miRLocator performed better than the state-of-art miRNA predictor miRdup in locating mature miRNAs within plant pre-miRNAs. miRLocator will aid researchers interested in discovering miRNAs from model and non-model plant species.
| S-EPMC4641693 | biostudies-literature
Project description:BackgroundMicroRNA (miRNA) directed gene repression is an important mechanism of posttranscriptional regulation. Comprehensive analyses of how microRNA influence biological processes requires paired miRNA-mRNA expression datasets. However, a review of both GEO and ArrayExpress repositories revealed few such datasets, which was in stark contrast to the large number of messenger RNA (mRNA) only datasets. It is of interest that numerous primary miRNAs (precursors of microRNA) are known to be co-expressed with coding genes (host genes).ResultsWe developed a miRNA-mRNA interaction analyses pipeline. The proposed solution is based on two miRNA expression prediction methods - a scaling function and a linear model. Additionally, miRNA-mRNA anti-correlation analyses are used to determine the most probable miRNA gene targets (i.e. the differentially expressed genes under the influence of up- or down-regulated microRNA). Both the consistency and accuracy of the prediction method is ensured by the application of stringent statistical methods. Finally, the predicted targets are subjected to functional enrichment analyses including GO, KEGG and DO, to better understand the predicted interactions.ConclusionsThe MMpred pipeline requires only mRNA expression data as input and is independent of third party miRNA target prediction methods. The method passed extensive numerical validation based on the binding energy between the mature miRNA and 3' UTR region of the target gene. We report that MMpred is capable of generating results similar to that obtained using paired datasets. For the reported test cases we generated consistent output and predicted biological relationships that will help formulate further testable hypotheses.
| S-EPMC3562514 | biostudies-literature
Project description:Bioinformatic approaches have complemented experimental efforts to inventorize plant miRNA targets. We carried out global computational analysis of rice (Oryza sativa) transcriptome to generate a comprehensive list of putative miRNA targets. Our predictions (684 unique transcripts) showed that rice miRNAs mediate regulation of diverse functions including transcription (41%), catalysis (28%), binding (18%), and transporter activity (11%). Among the predicted targets, 61.7% hits were in coding regions and nearly 72% targets had a solitary miRNA hit. The study predicted more than 70 novel targets of 34 miRNAs putatively regulating functions like stress-response, catalysis, and binding. It was observed that more than half (55%) of the targets were conserved between O. sativa indica and O. sativa japonica. Members of 31 miRNA families were found to possess conserved targets between rice and at least one of other grass family members. About 44% of the unique targets were common between two dissimilar miRNA prediction algorithms. Such an extent of cross-species conservation and algorithmic consensus confers confidence in the list of rice miRNA targets predicted in this study.
| S-EPMC5054203 | biostudies-literature
Project description:MicroRNAs (miRNAs) have been confirmed to be closely related to various human complex diseases by many experimental studies. It is necessary and valuable to develop powerful and effective computational models to predict potential associations between miRNAs and diseases. In this work, we presented a prediction model of Graphlet Interaction for MiRNA-Disease Association prediction (GIMDA) by integrating the disease semantic similarity, miRNA functional similarity, Gaussian interaction profile kernel similarity and the experimentally confirmed miRNA-disease associations. The related score of a miRNA to a disease was calculated by measuring the graphlet interactions between two miRNAs or two diseases. The novelty of GIMDA lies in that we used graphlet interaction to analyse the complex relationships between two nodes in a graph. The AUCs of GIMDA in global and local leave-one-out cross-validation (LOOCV) turned out to be 0.9006 and 0.8455, respectively. The average result of five-fold cross-validation reached to 0.8927 ± 0.0012. In case study for colon neoplasms, kidney neoplasms and prostate neoplasms based on the database of HMDD V2.0, 45, 45, 41 of the top 50 potential miRNAs predicted by GIMDA were validated by dbDEMC and miR2Disease. Additionally, in the case study of new diseases without any known associated miRNAs and the case study of predicting potential miRNA-disease associations using HMDD V1.0, there were also high percentages of top 50 miRNAs verified by the experimental literatures.
| S-EPMC5824414 | biostudies-literature
Project description:Recently, accumulating evidences have indicated miRNAs play critical roles in the progression and development of various human complex diseases, which pointed out that identifying miRNA-disease association could enable us to understand diseases at miRNA level. Thus, revealing more and more potential miRNA-disease associations is a vital topic in biomedical domain. However, it will be extremely expensive and time-consuming if we examine all the possible miRNA-disease pairs. Therefore, more accurate and efficient methods are being highly requested to detect potential miRNA-disease associations. In this study, we developed a computational model of Ensemble Learning and Link Prediction for miRNA-Disease Association prediction (ELLPMDA) to achieve this goal. By integrating miRNA functional similarity, disease semantic similarity, miRNA-disease association and Gaussian profile kernel similarity for miRNAs and diseases, we constructed a similarity network and utilized ensemble learning to combine rank results given by three classic similarity-based algorithms. To evaluate the performance of ELLPMDA, we exploited global and local Leave-One-Out Cross Validation (LOOCV), 5-fold Cross Validation (CV) and three kinds of case studies. As a result, the AUCs of ELLPMDA is 0.9181, 0.8181 and 0.9193+/-0.0002 in global LOOCV, local LOOCV and 5-fold CV, respectively, which significantly exceed almost all the previous methods. Moreover, in three distinct kinds of case studies for Kidney Neoplasms, Lymphoma, Prostate Neoplasms, Colon Neoplasms and Esophageal Neoplasms, 88%, 92%, 86%, 98% and 98% out of the top 50 predicted miRNAs has been confirmed, respectively. Besides, ELLPMDA is based on global similarity measure and applicable to new diseases without any known related miRNAs.
| S-EPMC6152467 | biostudies-literature
Project description:MicroRNAs (miRNAs) suppress gene expression by forming a duplex with a target messenger RNA (mRNA), blocking translation or initiating cleavage. Computational approaches have proven valuable for predicting which mRNAs can be targeted by a given miRNA, but currently available prediction methods do not address the extent of duplex formation under physiological conditions. Some miRNAs can at low concentrations bind to target mRNAs, whereas others are unlikely to bind within a physiologically relevant concentration range. Here we present a novel approach in which we find potential target sites on mRNA that minimize the calculated free energy of duplex formation, compute the free energy change involved in unfolding these sites, and use these energies to estimate the extent of duplex formation at specified initial concentrations of both species. We compare our predictions to experimentally confirmed miRNA-mRNA interactions (and non-interactions) in Drosophila melanogaster and in human. Although our method does not predict whether the targeted mRNA is degraded and/or its translation to protein inhibited, our quantitative estimates generally track experimentally supported results, indicating that this approach can be used to predict whether an interaction occurs at specified concentrations. Our approach offers a more-quantitative understanding of post-translational regulation in different cell types, tissues, and developmental conditions.
| S-EPMC3044769 | biostudies-literature
Project description:MotivationIdentifying drug-target interactions is an important task in drug discovery. To reduce heavy time and financial cost in experimental way, many computational approaches have been proposed. Although these approaches have used many different principles, their performance is far from satisfactory, especially in predicting drug-target interactions of new candidate drugs or targets.MethodsApproaches based on machine learning for this problem can be divided into two types: feature-based and similarity-based methods. Learning to rank is the most powerful technique in the feature-based methods. Similarity-based methods are well accepted, due to their idea of connecting the chemical and genomic spaces, represented by drug and target similarities, respectively. We propose a new method, DrugE-Rank, to improve the prediction performance by nicely combining the advantages of the two different types of methods. That is, DrugE-Rank uses LTR, for which multiple well-known similarity-based methods can be used as components of ensemble learning.ResultsThe performance of DrugE-Rank is thoroughly examined by three main experiments using data from DrugBank: (i) cross-validation on FDA (US Food and Drug Administration) approved drugs before March 2014; (ii) independent test on FDA approved drugs after March 2014; and (iii) independent test on FDA experimental drugs. Experimental results show that DrugE-Rank outperforms competing methods significantly, especially achieving more than 30% improvement in Area under Prediction Recall curve for FDA approved new drugs and FDA experimental drugs.Availabilityhttp://datamining-iip.fudan.edu.cn/service/DrugE-RankContactzhusf@fudan.edu.cnSupplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC4908328 | biostudies-literature
Project description:BackgroundmicroRNAs (miRNAs) have been shown to play essential roles in a wide range of biological processes. Many computational methods have been developed to identify targets of miRNAs. However, the majority of these methods depend on pre-defined features that require considerable efforts and resources to compute and often prove suboptimal at predicting miRNA targets.ResultsWe developed a novel hybrid deep learning-based (DL-based) approach that is capable of predicting miRNA targets at a higher accuracy. This approach integrates convolutional neural networks (CNNs) that excel in learning spatial features and recurrent neural networks (RNNs) that discern sequential features. Therefore, our approach has the advantages of learning both the intrinsic spatial and sequential features of miRNA:target. The inputs for our approach are raw sequences of miRNAs and genes that can be obtained effortlessly. We applied our approach on two human datasets from recently miRNA target prediction studies and trained two models. We demonstrated that the two models consistently outperform the previous methods according to evaluation metrics on test datasets. Comparing our approach with currently available alternatives on independent datasets shows that our approach delivers substantial improvements in performance. We also show with multiple evidences that our approach is more robust than other methods on small datasets. Our study is the first study to perform comparisons across multiple existing DL-based approaches on miRNA target prediction. Furthermore, we examined the contribution of a Max pooling layer in between the CNN and RNN and demonstrated that it improves the performance of all our models. Finally, a unified model was developed that is robust on fitting different input datasets.ConclusionsWe present a new DL-based approach for predicting miRNA targets and demonstrate that our approach outperforms the current alternatives. We supplied an easy-to-use tool, miTAR, at https://github.com/tjgu/miTAR . Furthermore, our analysis results support that Max Pooling generally benefits the hybrid models and potentially prevents overfitting for hybrid models.
| S-EPMC7912887 | biostudies-literature
Project description:MicroRNAs (miRNAs) are a kind of noncoding RNA, which plays an essential role in gene regulation by binding to messenger RNAs (mRNAs). Accurate and rapid identification of miRNA target genes is helpful to reveal the mechanism of transcriptome regulation, which is of great significance for the study of cancer and other diseases. Many bioinformatics methods have been proposed to solve this problem, but the previous research did not further study the encoding of the nucleotide sequence. In this paper, we developed a novel method combining word embedding and deep learning for human miRNA targets at the site-level prediction, which is inspired by the similarity between natural language and biological sequences. First, the word2vec model was used to mine the distribution representation of miRNAs and mRNAs. Then, the embedding is extracted automatically via the stacked bidirectional long short-term memory (BiLSTM) network. By testing, our method can effectively improve the accuracy, sensitivity, specificity, and F-measure of other methods. Through our research, it is proved that the distributed representation can improve the accuracy of the deep learning model and better solve the miRNA target site prediction problem.
| S-EPMC9343202 | biostudies-literature