Computational Prediction of Functional MicroRNA-mRNA Interactions
| dc.contributor.author | Demirci, Muserref Duygu Sacar | |
| dc.contributor.author | Yousef, Malik | |
| dc.contributor.author | Allmer, Jens | |
| dc.date.accessioned | 2025-09-25T10:43:04Z | |
| dc.date.available | 2025-09-25T10:43:04Z | |
| dc.date.issued | 2019 | |
| dc.description | Allmer, Jens/0000-0002-2164-7335; Sacar Demirci, Muserref Duygu/0000-0003-2012-0598; | en_US |
| dc.description.abstract | Proteins have a strong influence on the phenotype and their aberrant expression leads to diseases. MicroRNAs (miRNAs) are short RNA sequences which posttranscriptionally regulate protein expression. This regulation is driven by miRNAs acting as recognition sequences for their target mRNAs within a larger regulatory machinery. A miRNA can have many target mRNAs and an mRNA can be targeted by many miRNAs which makes it difficult to experimentally discover all miRNA-mRNA interactions. Therefore, computational methods have been developed for miRNA detection and miRNA target prediction. An abundance of available computational tools makes selection difficult. Additionally, interactions are not currently the focus of investigation although they more accurately define the regulation than pre-miRNA detection or target prediction could perform alone. We define an interaction including the miRNA source and the mRNA target. We present computational methods allowing the investigation of these interactions as well as how they can be used to extend regulatory pathways. Finally, we present a list of points that should be taken into account when investigating miRNA-mRNA interactions. In the future, this may lead to better understanding of functional interactions which may pave the way for disease marker discovery and design of miRNA-based drugs. | en_US |
| dc.identifier.doi | 10.1007/978-1-4939-8982-9_7 | |
| dc.identifier.isbn | 9781493989829 | |
| dc.identifier.isbn | 9781493989812 | |
| dc.identifier.issn | 1064-3745 | |
| dc.identifier.issn | 1940-6029 | |
| dc.identifier.scopus | 2-s2.0-85059888357 | |
| dc.identifier.uri | https://doi.org/10.1007/978-1-4939-8982-9_7 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12573/3519 | |
| dc.language.iso | en | en_US |
| dc.publisher | Humana Press Inc | en_US |
| dc.relation.ispartof | Methods in Molecular Biology | en_US |
| dc.relation.ispartofseries | Methods in Molecular Biology | |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.subject | MicroRNA | en_US |
| dc.subject | Target | en_US |
| dc.subject | Regulation | en_US |
| dc.subject | Posttranscriptional Regulation | en_US |
| dc.subject | Pathway Extension | en_US |
| dc.subject | miRNA-mRNA Interaction | en_US |
| dc.title | Computational Prediction of Functional MicroRNA-mRNA Interactions | en_US |
| dc.type | Book Part | en_US |
| dspace.entity.type | Publication | |
| gdc.author.id | Allmer, Jens/0000-0002-2164-7335 | |
| gdc.author.id | Sacar Demirci, Muserref Duygu/0000-0003-2012-0598 | |
| gdc.author.scopusid | 55735789200 | |
| gdc.author.scopusid | 14029389000 | |
| gdc.author.scopusid | 24821311300 | |
| gdc.author.wosid | Allmer, Jens/E-2335-2016 | |
| gdc.author.wosid | Sacar Demirci, Muserref Duygu/N-7458-2017 | |
| gdc.author.wosid | Demirci, Müşerref/N-7458-2017 | |
| gdc.bip.impulseclass | C4 | |
| gdc.bip.influenceclass | C5 | |
| gdc.bip.popularityclass | C4 | |
| gdc.coar.access | metadata only access | |
| gdc.coar.type | text::book::book part | |
| gdc.collaboration.industrial | false | |
| gdc.description.department | Abdullah Gül University | en_US |
| gdc.description.departmenttemp | [Demirci, Muserref Duygu Sacar] Abdullah Gul Univ, Dept Bioinformat, Fac Life & Nat Sci, Kayseri, Turkey; [Yousef, Malik] Zefat Acad Coll, Dept Community Informat Syst, Safed, Israel; [Allmer, Jens] Wageningen Univ & Res, Appl Bioinformat, Biosci, Wageningen, Netherlands | en_US |
| gdc.description.endpage | 196 | en_US |
| gdc.description.publicationcategory | Kitap Bölümü - Uluslararası | en_US |
| gdc.description.scopusquality | Q4 | |
| gdc.description.startpage | 175 | en_US |
| gdc.description.volume | 1912 | en_US |
| gdc.description.woscitationindex | Book Citation Index – Science | |
| gdc.description.wosquality | N/A | |
| gdc.identifier.openalex | W2910782067 | |
| gdc.identifier.pmid | 30635894 | |
| gdc.identifier.wos | WOS:000620741400008 | |
| gdc.index.type | WoS | |
| gdc.index.type | Scopus | |
| gdc.index.type | PubMed | |
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| gdc.oaire.keywords | Target | |
| gdc.oaire.keywords | Sequence Analysis, RNA | |
| gdc.oaire.keywords | Gene Expression Profiling | |
| gdc.oaire.keywords | Computational Biology | |
| gdc.oaire.keywords | High-Throughput Nucleotide Sequencing | |
| gdc.oaire.keywords | MicroRNA | |
| gdc.oaire.keywords | Pathway extension | |
| gdc.oaire.keywords | MiRNA–mRNA interaction | |
| gdc.oaire.keywords | Machine Learning | |
| gdc.oaire.keywords | MicroRNAs | |
| gdc.oaire.keywords | Databases, Genetic | |
| gdc.oaire.keywords | Animals | |
| gdc.oaire.keywords | Humans | |
| gdc.oaire.keywords | Gene Regulatory Networks | |
| gdc.oaire.keywords | RNA, Messenger | |
| gdc.oaire.keywords | Posttranscriptional regulation | |
| gdc.oaire.keywords | Software | |
| gdc.oaire.keywords | Regulation | |
| gdc.oaire.popularity | 1.5771462E-8 | |
| gdc.oaire.publicfunded | false | |
| gdc.oaire.sciencefields | 0301 basic medicine | |
| gdc.oaire.sciencefields | 0303 health sciences | |
| gdc.oaire.sciencefields | 03 medical and health sciences | |
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| gdc.openalex.normalizedpercentile | 0.97 | |
| gdc.openalex.toppercent | TOP 10% | |
| gdc.opencitations.count | 23 | |
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| gdc.plumx.mendeley | 26 | |
| gdc.plumx.pubmedcites | 15 | |
| gdc.plumx.scopuscites | 26 | |
| gdc.scopus.citedcount | 26 | |
| gdc.virtual.author | Saçar Demirci, Müşerref Duygu | |
| gdc.wos.citedcount | 19 | |
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