Relevant bibliographies by topics / MiRNA-mRNA interaction prediction

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Academic literature on the topic 'MiRNA-mRNA interaction prediction'

Author: Grafiati
Published: 13 April 2024

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Journal articles on the topic "MiRNA-mRNA interaction prediction"

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Stempor, Przemyslaw A., Michael Cauchi, and Paul Wilson. "MMpred: functional miRNA – mRNA interaction analyses by miRNA expression prediction." BMC Genomics 13, no. 1 (2012): 620. http://dx.doi.org/10.1186/1471-2164-13-620.

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Plotnikova, O. M., and M. Y. Skoblov. "Efficiency of the miRNA–mRNA Interaction Prediction Programs." Molecular Biology 52, no. 3 (May 2018): 467–77. http://dx.doi.org/10.1134/s0026893318020103.

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Wang, Zixing, Wenlong Xu, Haifeng Zhu, and Yin Liu. "A Bayesian Framework to Improve MicroRNA Target Prediction by Incorporating External Information." Cancer Informatics 13s7 (January 2014): CIN.S16348. http://dx.doi.org/10.4137/cin.s16348.

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MicroRNAs (miRNAs) are small regulatory RNAs that play key gene-regulatory roles in diverse biological processes, particularly in cancer development. Therefore, inferring miRNA targets is an essential step to fully understanding the functional properties of miRNA actions in regulating tumorigenesis. Bayesian linear regression modeling has been proposed for identifying the interactions between miRNAs and mRNAs on the basis of the integrated sequence information and matched miRNA and mRNA expression data; however, this approach does not use the full spectrum of available features of putative miRNA targets. In this study, we integrated four important sequence and structural features of miRNA targeting with paired miRNA and mRNA expression data to improve miRNA-target prediction in a Bayesian framework. We have applied this approach to a gene-expression study of liver cancer patients and examined the posterior probability of each miRNA-mRNA interaction being functional in the development of liver cancer. Our method achieved better performance, in terms of the number of true targets identified, than did other methods.
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Fang, Yi, Xiaoyong Pan, and Hong-Bin Shen. "Recent Deep Learning Methodology Development for RNA–RNA Interaction Prediction." Symmetry 14, no. 7 (June 23, 2022): 1302. http://dx.doi.org/10.3390/sym14071302.

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Genetic regulation of organisms involves complicated RNA–RNA interactions (RRIs) among messenger RNA (mRNA), microRNA (miRNA), and long non-coding RNA (lncRNA). Detecting RRIs is beneficial for discovering biological mechanisms as well as designing new drugs. In recent years, with more and more experimentally verified RNA–RNA interactions being deposited into databases, statistical machine learning, especially recent deep-learning-based automatic algorithms, have been widely applied to RRI prediction with remarkable success. This paper first gives a brief introduction to the traditional machine learning methods applied on RRI prediction and benchmark databases for training the models, and then provides a recent methodology overview of deep learning models in the prediction of microRNA (miRNA)–mRNA interactions and long non-coding RNA (lncRNA)–miRNA interactions.
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Ragan, Chikako, Michael Zuker, and Mark A. Ragan. "Quantitative Prediction of miRNA-mRNA Interaction Based on Equilibrium Concentrations." PLoS Computational Biology 7, no. 2 (February 24, 2011): e1001090. http://dx.doi.org/10.1371/journal.pcbi.1001090.

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Kondybayeva, Аida, Aigul Akimniyazova, Saltanat Kamenova, Gulsum Duchshanova, Dana Aisina, Alla Goncharova, and Аnatoliy Ivashchenko. "Prediction of miRNA interaction with mRNA of stroke candidate genes." Neurological Sciences 41, no. 4 (November 30, 2019): 799–808. http://dx.doi.org/10.1007/s10072-019-04158-x.

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Li, Yameng, Yukun Xu, Yawei Hou, and Rui Li. "Construction and Bioinformatics Analysis of the miRNA-mRNA Regulatory Network in Diabetic Nephropathy." Journal of Healthcare Engineering 2021 (November 18, 2021): 1–11. http://dx.doi.org/10.1155/2021/8161701.

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Background. MicroRNA (miRNA) has been confirmed to be involved in the occurrence, development, and prevention of diabetic nephropathy (DN), but its mechanism of action is still unclear. Objective. With the help of the GEO database, bioinformatics methods are used to explore the miRNA-mRNA regulatory relationship pairs related to diabetic nephropathy and explain their potential mechanisms of action. Methods. The DN-related miRNA microarray dataset (GSE51674) and mRNA expression dataset (GSE30122) are downloaded through the GEO database, online analysis tool GEO2R is used for data differential expression analysis, TargetScan, miRTarBase, and miRDB databases are used to predict potential downstream target genes regulated by differentially expressed miRNAs, and intersection with differential genes is used to obtain candidate target genes. According to the regulatory relationship between miRNA and mRNA, the miRNA-mRNA relationship pair is clarified, and the miRNA-mRNA regulatory network is constructed using Cytoscape. DAVID is used to perform GO function enrichment analysis and KEGG pathway analysis of candidate target genes. By GeneMANIA prediction of miRNA target genes and coexpressed genes, the protein interaction network is constructed. Results and Conclusions. A total of 67 differentially expressed miRNAs were screened in the experiment, of which 42 were upregulated and 25 were downregulated; a total of 448 differentially expressed mRNAs were screened, of which 93 were upregulated and 355 were downregulated. Using TargetScan, miRTarBase, and miRDB databases to predict downstream targets of differentially expressed miRNAs, 2283 downstream target genes coexisting in 3 databases were predicted to intersect with differentially expressed mRNAs to obtain 96 candidate target genes. Finally, 44 miRNA-mRNA relationship pairs consisting of 12 differentially expressed miRNAs and 27 differentially expressed mRNAs were screened out; further analysis showed that miRNA regulatory network genes may participate in the occurrence and development of diabetic nephropathy through PI3K/Akt, ECM-receptor interaction pathway, and RAS signaling pathway.
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Sweef, Osama, Chengfeng Yang, and Zhishan Wang. "The Oncogenic and Tumor Suppressive Long Non-Coding RNA–microRNA–Messenger RNA Regulatory Axes Identified by Analyzing Multiple Platform Omics Data from Cr(VI)-Transformed Cells and Their Implications in Lung Cancer." Biomedicines 10, no. 10 (September 20, 2022): 2334. http://dx.doi.org/10.3390/biomedicines10102334.

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Chronic exposure to hexavalent chromium (Cr(VI)) causes lung cancer in humans, however, the underlying mechanism has not been well understood. Long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) are commonly studied non-coding RNAs. miRNAs function mainly through interaction with the 3′-untranslated regions of messenger RNAs (mRNAs) to down-regulate gene expression. LncRNAs have been shown to function as competing endogenous RNAs (ceRNAs) to sponge miRNAs and regulate gene expression. It is now well accepted that lncRNAs and miRNAs could function as oncogenes or tumor suppressors. Dysregulations of lncRNAs and miRNAs have been shown to play important roles in cancer initiation, progression, and prognosis. To explore the mechanism of Cr(VI) lung carcinogenesis, we performed lncRNA, mRNA, and miRNA microarray analysis using total RNAs from our previously established chronic Cr(VI) exposure malignantly transformed and passage-matched control human bronchial epithelial BEAS-2B cells. Based on the differentially expressed lncRNAs, miRNAs, and mRNAs between the control (BEAS-2B-Control) and Cr(VI)-transformed (BEAS-Cr(VI)) cells and by using the lncRNA–miRNA interaction and miRNA target prediction algorithms, we identified three oncogenic (HOTAIRM1/miR-182-5p/ERO1A, GOLGA8B/miR-30d-5p/RUNX2, and PDCD6IPP2/miR-23a-3p/HOXA1) and three tumor suppressive (ANXA2P1/miR-20b-5p/FAM241A (C4orf32), MIR99AHG/miR-218-5p/GPM6A, and SH3RF3-AS1/miR-34a-5p/HECW2) lncRNA–miRNA–mRNA regulatory axes. Moreover, the relevance of these three oncogenic and three tumor suppressive lncRNA–miRNA–mRNA regulatory axes in lung cancer was explored by analyzing publicly available human lung cancer omics datasets. It was found that the identified three oncogenic lncRNA–miRNA–mRNA regulatory axes (HOTAIRM1/miR-182-5p/ERO1A, GOLGA8B/miR-30d-5p/RUNX2, and PDCD6IPP2/miR-23a-3p/HOXA1) and the three tumor suppressive lncRNA–miRNA–mRNA regulatory axes (ANXA2P1/miR-20b-5p/FAM241A (C4orf32), MIR99AHG/miR-218-5p/GPM6A, and SH3RF3-AS1/miR-34a-5p/HECW2) have significant diagnostic and prognosis prediction values in human lung cancer. In addition, our recent studies showed that Cr(VI)-transformed cells display cancer stem cell (CSC)-like properties. Further bioinformatics analysis identified the oncogenic lncRNA–miRNA–mRNA regulatory axes as the potential regulators of cancer stemness. In summary, our comprehensive analysis of multiple platform omics datasets obtained from Cr(VI)-transformed human bronchial epithelial cells identified several oncogenic and tumor suppressive lncRNA–miRNA–mRNA regulatory axes, which may play important roles in Cr(VI) carcinogenesis and lung cancer in general.
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Wei, Jiabo, Haihong Zhu, Qijun Zhang, and Qin Zhang. "Prediction of Functional Genes in Primary Varicose Great Saphenous Veins Using the lncRNA-miRNA-mRNA Network." Computational and Mathematical Methods in Medicine 2022 (September 8, 2022): 1–14. http://dx.doi.org/10.1155/2022/4722483.

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Background. Long noncoding RNAs (lncRNAs) have been widely suggested to bind with the microRNA (miRNA) sites and play roles of competing endogenous RNAs (ceRNAs), which can thus affect and regulate target gene and mRNA expression. Such lncRNA-related ceRNAs are identified to exert vital parts in vascular disease. Nonetheless, it remains unknown about how the lncRNA-miRNA-mRNA network functions in the varicose great saphenous veins. Methods. This study acquired the lncRNA and mRNA expression patterns from the GEO database and identifies the differentially expressed mRNAs and lncRNAs by adopting the R software “limma” package. Then, miRcode, miRDB, miRTarbase, and TargetScan were used to establish the miRNA-mRNA pairs and lncRNA-miRNA pairs. In addition, the lncRNA-miRNA-mRNA ceRNA network was constructed by using Cytoscape. Protein-protein interaction, Gene Ontology functional annotations, and Kyoto Encyclopedia of Genes and Genomes enrichment were carried out to examine the candidate hub genes, the functions of genes, and the corresponding pathways. Results. In line with the preset theory, we constructed ceRNA network comprising 12 lncRNAs, 38 miRNAs, and 149 mRNAs. Kyoto Encyclopedia of Genes and Genomes analysis indicated that the PI3K/Akt signaling pathway played a vital part in the development of varicose great saphenous veins. AC114730, AC002127, and AC073342 were significant biomarkers. At the same time, we predicted the potential miRNA, which may exert a significant influence on the varicose great saphenous veins, namely, miR-17-5p, miR-129-5p, miR-1297, miR-20b-5p, and miR-33a-3p. Conclusion. By performing ceRNA network analysis, our study detects new lncRNAs, miRNAs, and mRNAs, which can be applied as underlying biomarkers of varicose great saphenous veins and as therapeutic targets for the treatment of varicose great saphenous veins.
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Chen, Jiajia, and Liangzhi Li. "Multiple Regression Analysis Reveals MicroRNA Regulatory Networks in Oryza sativa under Drought Stress." International Journal of Genomics 2018 (October 4, 2018): 1–12. http://dx.doi.org/10.1155/2018/9395261.

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Drought is a major abiotic stress that reduces rice development and yield. miRNAs (microRNAs) are known to mediate posttranscriptional regulation under drought stress. Although the importance of individual miRNAs has been established, the crosstalks between miRNAs and mRNAs remain unearthed. Here we performed microarray analysis of miRNAs and matched mRNA expression profiles of drought-treated rice cultivar Nipponbare. Drought-responsive miRNA-mRNA regulations were identified by a combination of a partial least square (PLS) regression approach and sequence-based target prediction. A drought-induced network with 13 miRNAs and 58 target mRNAs was constructed, and four miRNA coregulatory modules were revealed. Functional analysis suggested that drought-response miRNA targets are enriched in hormone signaling, lipid and carbohydrate metabolism, and antioxidant defense. 13 candidate miRNAs and target genes were validated by RT-qPCR, hierarchical clustering, and ROC analysis. Two target genes (DWARF-3 and P0651G05.2) of miRNA coregulatory modules were further verified by RLM-5′ RACE. Together, our integrative study of miRNA-mRNA interaction provided attractive candidates that will help elucidate the drought-response mechanisms in Oryza sativa.
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Dissertations / Theses on the topic "MiRNA-mRNA interaction prediction"

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Homberg, Nicolas. "New models and algorithms for the identification of sncRNA-(snc)RNA interactions intra and across-species/kingdom." Electronic Thesis or Diss., Lyon 1, 2023. http://www.theses.fr/2023LYO10090.

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Les microARNs (miARNs) sont de petit ARNs non codant présents dans tous les eucaryotes qui régulent, positivement ou négativement, l'expression des ARN messagers (ARNms). Les miARNs ont un potentiel important pour de futurs traitements du cancer et d'autres maladies. Les interactions miARN-ARNm dépendent d'une variété de mécanismes complexes, tels que la complémentarité des séquences, l'accessibilité et la conservation. Cette thèse se concentre sur deux de ces mécanismes, à savoir l'accessibilité et la conservation intra-espèce du site d'interaction, en utilisant des données expérimentales de Cross-linking, Ligation And Sequencing of Hybrids (CLASH). Bien que l'accessibilité des sites d'interaction sur les ARNms soit généralement observée, cela n'est pas le cas pour toutes les interactions. La conservation intra-espèce est un mécanisme peu considéré que nous avons étudiée au travers la recherche de motifs conservés dans les ARNms. Bien que les résultats obtenus soient bruités, il est possible de retrouver via ces motifs certains sites d'interaction sur les ARNms
MicroRNAs (miRNAs) are non-coding RNAs present in eukaryotes that regulate the expression of messenger RNAs (mRNAs) up or down. These miRNAs have significant potential in future treatment of cancer and other diseases. The miRNA-mRNA interactions are intricate and involve various mechanisms, such as sequence complementarity, accessibility, and conservation. This thesis focuses on two such mechanisms, namely accessibility and intra-species conservation of the site of interaction, using experimental data from Cross-linking, Ligation And Sequencing of Hybrids (CLASH). Although the accessibility of interaction sites on mRNAs is generally observed, it is not consistent for all interactions. Intra-species conservation is a rare feature, which we explore by inferring conserved motifs from mRNA interaction sites. Although the results are noisy, in some specific cases, we manage to retrieve some mRNA interaction sites from the inferred motifs
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Book chapters on the topic "MiRNA-mRNA interaction prediction"

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Andrés-León, Eduardo, Gonzalo Gómez-López, and David G. Pisano. "Prediction of miRNA–mRNA Interactions Using miRGate." In Methods in Molecular Biology, 225–37. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4939-6866-4_15.

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Thi Ngoc Nguyen, Thanh, Thu Huynh Ngoc Nguyen, Luan Huu Huynh, Hoang Ngo Phan, and Hue Thi Nguyen. "Predicting SNPs in Mature MicroRNAs Dysregulated in Breast Cancer." In Recent Advances in Non-Coding RNAs [Working Title]. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.105514.

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Breast cancer (BC) is the leading type of cancer among women. Findings have revolutionized current knowledge of microRNA (miRNA) in breast tumorigenesis. The seed region of miRNA regulates the process of gene expression negatively. The presence of SNPs in the seed regions of miRNA dramatically alters the mature miRNA function. Additionally, SNPs in the out-seed region of miRNAs have a significant impact on miRNA targeting. This study focuses on the in silico analysis procedure of mature miRNA SNPs and their impact on BC risk. The database annotated SNPs on mature miRNAs was used. Also, target gene alterations, miRNAs function in BC, and the interaction of miRNAs with targets were predicted. A list of 101 SNPs in 100 miRNAs with functional targets in BC was indicated. Under the SNPs allele variation, 10 miRNAs changed function, 6 miRNAs lost targets, 15 miRNAs gained targets, 48 onco-miRNAs remained unchanged, and 21 tumor suppressor miRNAs remained unchanged. At last, a list of 89 SNPs, which alter miRNA function and miRNA-mRNA interaction, were shown to be potentially associated with BC risk. This research theoretically generated a list of possible causative SNPs in the mature miRNA gene that might be used in future BC management studies.
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