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dc.contributor.advisorAllmer, Jensen
dc.contributor.authorSaçar, Müşerref Duygu-
dc.descriptionThesis (Master)--Izmir Institute of Technology, Molecular Biology and Genetics, Izmir, 2013en
dc.descriptionIncludes bibliographical references (leaves: 34-41)en
dc.descriptionText in English; Abstract: Turkish an Englishen
dc.descriptionix, 41 leavesen
dc.descriptionFull text release delayed at author's request until 2017.01.13en
dc.description.abstractMicroRNAs (miRNAs) are single-stranded, small, usually non-coding RNAs of about 22 nucleotides in length, that control gene expression at the posttranscriptional level through translational inhibition, degradation, adenylation, or destabilization of their target mRNAs. Although hundreds of miRNAs have been identified in various species, many more may still remain unknown. Therefore, the discovery of new miRNA genes is an important step for understanding miRNA mediated post transcriptional regulation mechanisms. First attempts for the identification of novel miRNA genes were almost exclusively based on directional cloning of endogenous small RNAs and high-throughput sequencing of large numbers of cDNA clones. However, conventional forward genetic screening is known to be biased towards abundantly and/or ubiquitously expressed miRNAs that can dominate the cloned products. Hence, such biological approaches might be limited in their ability to detect rare miRNAs, and restricted to the tissues and the developmental stage of the organism under examination. These limitations have led to the development of sophisticated computational approaches attempting to identify possible miRNAs in silico. Nevertheless, the programs designed to predict possible miRNAs in a genome are not sensitive or accurate enough to warrant sufficient confidence for validating all their predictions experimentally. With this study, we aim to solve these problems by developing a new and sensitive machine learning based approach to predict potential miRNAs in the human genome.en
dc.publisherIzmir Institute of Technologyen
dc.subject.lcshRNA editingen
dc.subject.lcshSmall interfering RNAen
dc.subject.lcshData miningen
dc.titleAn integrative data mining approach for microRNA detection in humanen_US
dc.typeMaster Thesisen_US
dc.institutionauthorSaçar, Müşerref Duygu-
dc.departmentIzmir Institute of Technology. Molecular Biology and Geneticsen_US
item.openairetypeMaster Thesis-
item.fulltextWith Fulltext-
Appears in Collections:Master Degree / Yüksek Lisans Tezleri
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