Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/5643
Title: Machine learning methods for microRNA gene prediction
Authors: Saçar, Müşerref Duygu
Allmer, Jens
Keywords: MicroRNAs
Artificial intelligence
Algorithms
Genes
Machine learning
Classification
Issue Date: 2014
Publisher: Humana Press
Source: Saçar, M. D., and Allmer, J. (2014). Machine learning methods for microRNA gene prediction. Methods in Molecular Biology, 1107, 177-187. doi:10.1007/978-1-62703-748-8-10
Abstract: MicroRNAs (miRNAs) are single-stranded, small, noncoding RNAs of about 22 nucleotides in length, which 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, discovery of new miRNA genes is an important step for understanding miRNA-mediated posttranscriptional regulation mechanisms. It seems that biological approaches to identify miRNA genes might be limited in their ability to detect rare miRNAs and are further limited to the tissues examined 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. In this chapter, we discuss computational problems in miRNA prediction studies and review some of the many machine learning methods that have been tried to address the issues.
URI: http://hdl.handle.net/11147/5643
http://doi.org/10.1007/978-1-62703-748-8_10
ISSN: 1940-6029
1064-3745
Appears in Collections:Molecular Biology and Genetics / Moleküler Biyoloji ve Genetik
PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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