ArticleBMC bioinformatics2010
Promoter prediction in E. coli based on SIDD profiles and Artificial Neural Networks.
Article in BMC bioinformatics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Prediction of essential genes in prokaryote based on artificial neural network.Genes & genomics · 2020Article
- Distinguishing between productive and abortive promoters using a random forest classifier in Mycoplasma pneumoniae.Nucleic acids research · 2015Article
- Recognition of prokaryotic promoters based on a novel variable-window Z-curve method.Nucleic acids research · 2012Article
- Proceedings of the 2011 MidSouth Computational Biology and Bioinformatics Society (MCBIOS) conference. Introduction.BMC bioinformatics · 2011Article
- Proceedings of the 2010 MidSouth Computational Biology and Bioinformatics Society (MCBIOS) conference.BMC bioinformatics · 2010Article
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3 authors.
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Abstract
backgroundOne 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.
resultsWhen 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.
conclusionsArtificial 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.
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