ArticleGenetics and molecular biology2011
Rules extraction from neural networks applied to the prediction and recognition of prokaryotic promoters.
Article in Genetics and molecular biology, 2011. 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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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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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
- Image-based promoter prediction: a promoter prediction method based on evolutionarily generated patterns.Scientific reports · 2018Article
- 70ProPred: a predictor for discovering sigma70 promoters based on combining multiple features.BMC systems biology · 2018Article
- Assessing the effects of data selection and representation on the development of reliable E. coli sigma 70 promoter region predictors.PloS one · 2015Article
- Quantitative design of regulatory elements based on high-precision strength prediction using artificial neural network.PloS one · 2013Article
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Authors and funding
3 authors.
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Abstract
Promoters are DNA sequences located upstream of the gene region and play a central role in gene expression. Computational techniques show good accuracy in gene prediction but are less successful in predicting promoters, primarily because of the high number of false positives that reflect characteristics of the promoter sequences. Many machine learning methods have been used to address this issue. Neural Networks (NN) have been successfully used in this field because of their ability to recognize imprecise and incomplete patterns characteristic of promoter sequences. In this paper, NN was used to predict and recognize promoter sequences in two data sets: (i) one based on nucleotide sequence information and (ii) another based on stability sequence information. The accuracy was approximately 80% for simulation (i) and 68% for simulation (ii). In the rules extracted, biological consensus motifs were important parts of the NN learning process in both simulations.
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