Promoter prediction using DNA numerical representation and neural network: Case study with three organisms

Swarna Bai Arniker, Hon Keung Kwan, Ngai Fong Law, Pak Kong Lun

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review


Promoter recognition in various organisms is an area of interest in bioinformatics. In this paper, a feed-forward neural network classifier is presented to predict promoters in three organisms using a DNA numerical representation approach. The proposed system was found to be able to predict promoters with a sensitivity of 87%, 87%, 99% while reducing false prediction rate for non-promoter sequences with a specificity of 92%, 94%, 99% for the human, Drosophila melanogaster, and Arabidopsis thaliana sequences respectively. The results show that feed-forward neural networks can extract the statistical characteristics of promoters efficiently, and that the 2-bit binary coding for DNA data is suitable for the Berkeley Human and Drosophila datasets and the 4-bit binary is suitable for the TAIR Arabidopsis thaliana data sets. Another result demonstrated here is that the proposed prediction system is reconfigurable and versatile with a reduced architecture and computational complexity.
Original languageEnglish
Title of host publicationProceedings - 2011 Annual IEEE India Conference
Subtitle of host publicationEngineering Sustainable Solutions, INDICON-2011
Publication statusPublished - 1 Dec 2011
Event2011 Annual IEEE India Conference: Engineering Sustainable Solutions, INDICON-2011 - Hyderabad, India
Duration: 16 Dec 201118 Dec 2011


Conference2011 Annual IEEE India Conference: Engineering Sustainable Solutions, INDICON-2011


  • Arabidopsis thaliana
  • bioinformatics
  • DNA numerical representation
  • Drosophila melanogaster
  • neural networks
  • promoter recognition

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment

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