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  • 标题:Predicting Untranslated Regions and Code Sections in DNA using Hidden Markov Models
  • 本地全文:下载
  • 作者:Tanvir Roushan ; Dipankar Chaki ; Abu Mohammad Hammad Ali
  • 期刊名称:International Journal of Computer and Information Technology
  • 印刷版ISSN:2279-0764
  • 出版年度:2014
  • 卷号:3
  • 期号:5
  • 页码:1134
  • 出版社:International Journal of Computer and Information Technology
  • 摘要:In experimental molecular biology, bioinformatics techniques such as image and signal processing allow extraction of useful results from large amounts of raw data. In the field of genetics and genomics, it aids in sequencing and annotating genomes. Given a biological sequence, such as a Deoxyribonucleic acid (DNA) sequence, biologists would like to analyze what that sequence represents. A challenging interest in computational biology at the moment is finding genes in DNA sequences. A DNA sequence consists of four nucleotide bases. There are two untranslated regions UTR5’ and UTR3’, which is not translated during the process of translation. The nucleotide base pair between UTR5’ and UTR3’ is known as the code section (CDS). Our goal is to find and develop a way to determine a likelihood value (using hidden Markov model), based on which the joining sections of these three regions can by identified in any given sequence.
  • 关键词:UTR5’; UTR3’; CDS splice sites; hidden Markov model; machine learning
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