Abstract:
BACKGROUND:One of the major challenges in biology is the correct identification of promoter regions. Computational methods based on motif searching have been the traditional approach taken. Recent studies have shown that DNA structural properties, such as curvature, stacking energy, and stress-induced duplex destabilization (SIDD) are useful in promoter prediction, as well. In this paper, the currently used SIDD energy threshold method is compared to the proposed artificial neural network (ANN) approach for finding promoters based on SIDD profile data. RESULTS:When compared to the SIDD threshold prediction method, artificial neural networks showed noticeable improvements for precision, recall, and F-score over a range of values. The maximal F-score for the ANN classifier was 62.3 and 56.8 for the threshold-based classifier. CONCLUSIONS:Artificial neural networks were used to predict promoters based on SIDD profile data. Results using this technique were an improvement over the previous SIDD threshold approach. Over a wide range of precision-recall values, artificial neural networks were more capable of identifying distinctive characteristics of promoter regions than threshold based methods.
journal_name
BMC Bioinformaticsjournal_title
BMC bioinformaticsauthors
Bland C,Newsome AS,Markovets AAdoi
10.1186/1471-2105-11-S6-S17subject
Has Abstractpub_date
2010-10-07 00:00:00pages
S17issn
1471-2105pii
1471-2105-11-S6-S17journal_volume
11 Suppl 6pub_type
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