Artificial neural networks based systems for recognition of genomic signals and regions: A review

Vladimir Bajic*, Suisheng Tang, Hao Han, Vladimir Brusic, Artemis G. Hatzigeorgiou

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

In this review we present a number of important applications in computational genomics of a class of intelligent systems, namely artificial neural networks (ANNs). We present the current state-of-the-art solutions used in recognition of different genomic signals and regions. All systems to be commented are based fully or in part on the ANNs. We included systems that recognize different aspects of a/ transcriptional control information related to promoters, TATA-box regulatory region, and polyA signal, b/ those that relate to translation process comprising recognition of the translation initiation site, coding cDNA/EST fragments, reading frame-shift errors and their correction, and c/ splice-sites recognition. The review includes some of the most efficient systems for the indicated recognition problems in bioinformatics and aims to be an initial guide for those interested in these challenging problems.

Original languageEnglish (US)
Pages (from-to)389-400
Number of pages12
JournalInformatica (Ljubljana)
Volume26
Issue number4
StatePublished - Dec 1 2002

Keywords

  • Artificial neural networks
  • Bioinformatics
  • Genomics

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Computer Science Applications
  • Artificial Intelligence

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