Machine Learning Methods for MicroRNA Gene Prediction

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Sacar M. D., Allmer J.

MIRNOMICS: MICRORNA BIOLOGY AND COMPUTATIONAL ANALYSIS, vol.1107, pp.177-187, 2014 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 1107
  • Publication Date: 2014
  • Doi Number: 10.1007/978-1-62703-748-8_10
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.177-187
  • Keywords: Machine learning, miRNA gene prediction, miRNA gene detection, Classification, Test data, Examples, SEQUENCE, IDENTIFICATION, CLASSIFICATION, FEATURES, GENOME, MODEL, RNA, BIOINFORMATICS, CONSERVATION, REGIONS
  • Abdullah Gül University Affiliated: No


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.