Artificial neural networks for prediction of mycobacterial promoter sequences

Kalate, Rupali N. ; Tambe, Sanjeev S. ; Kulkarni, Bhaskar D. (2003) Artificial neural networks for prediction of mycobacterial promoter sequences Computational Biology and Chemistry, 27 (6). pp. 555-564. ISSN 1476-9271

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Official URL: http://linkinghub.elsevier.com/retrieve/pii/S14769...

Related URL: http://dx.doi.org/10.1016/j.compbiolchem.2003.09.004

Abstract

A multilayered feed-forward ANN architecture trained using the error-back-propagation (EBP) algorithm has been developed for predicting whether a given nucleotide sequence is a mycobacterial promoter sequence. Owing to the high prediction capability (≅97%) of the developed network model, it has been further used in conjunction with the caliper randomization (CR) approach for determining the structurally/functionally important regions in the promoter sequences. The results obtained thereby indicate that: (i) upstream region of -35 box, (ii) -35 region, (iii) spacer region and, (iv) -10 box, are important for mycobacterial promoters. The CR approach also suggests that the -38 to -29 region plays a significant role in determining whether a given sequence is a mycobacterial promoter. In essence, the present study establishes ANNs as a tool for predicting mycobacterial promoter sequences and determining structurally/functionally important sub-regions therein.

Item Type:Article
Source:Copyright of this article belongs to Elsevier Science.
Keywords:Mycobacterial Promoters; Error-back-propagation Algorithm; Caliper Randomization Approach
ID Code:17191
Deposited On:16 Nov 2010 08:15
Last Modified:06 Jun 2011 09:05

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