Please use this identifier to cite or link to this item:
Title: DNA numerical representation and neural network based human promoter prediction system
Authors: Arniker, SB
Kwan, HK
Law, NF 
Lun, DPK 
Keywords: DNA numerical representation
Neural networks
Promoter recognition
Issue Date: 2011
Publisher: IEEE
Source: 2011 Annual IEEE India Conference (INDICON), 16-18 December 2011, Hyderabad, p. 1-4 How to cite?
Abstract: In spite of the recent development of computational methods for human promoter prediction, the prediction performance still needs improvement. In particular, the high false positive rate of the traditional approaches decreases the prediction reliability and leads to erroneous results in gene annotation. To improve the prediction accuracy and reliability, a DNA numerical representation and neural network based approach is studied for characterizing DNA alphabets in different regions of a DNA sequence. Three mapping functions are used for converting the DNA alphabets to numerical values so that discriminative biological features are extracted for promoter prediction. Simulations of the proposed system were carried out using a set of genomic sequences from the human chromosome 22 and it was found to achieve high sensitivity and specificity.
ISBN: 978-1-4577-1110-7
DOI: 10.1109/INDCON.2011.6139326
Appears in Collections:Conference Paper

View full-text via PolyU eLinks SFX Query
Show full item record


Last Week
Last month
Citations as of Aug 18, 2018

Page view(s)

Last Week
Last month
Citations as of Aug 14, 2018

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.