ArticleBMC bioinformatics2004
What can we learn from noncoding regions of similarity between genomes?
Article in BMC bioinformatics, 2004. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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Who cites it
2 citing papers in PubMed.
- Prediction of human functional genetic networks from heterogeneous data using RVM-based ensemble learning.Bioinformatics (Oxford, England) · 2010Article
- A machine learning strategy to identify candidate binding sites in human protein-coding sequence.BMC bioinformatics · 2006Article
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Authors and funding
2 authors.
Funding
Abstract
backgroundIn addition to known protein-coding genes, large amounts of apparently non-coding sequence are conserved between the human and mouse genomes. It seems reasonable to assume that these conserved regions are more likely to contain functional elements than less-conserved portions of the genome.
methodsHere we used a motif-oriented machine learning method based on the Relevance Vector Machine algorithm to extract the strongest signal from a set of non-coding conserved sequences.
resultsWe successfully fitted models to reflect the non-coding sequences, and showed that the results were quite consistent for repeated training runs. Using the learned models to scan genomic sequence, we found that they often made predictions close to the start of annotated genes. We compared this method with other published promoter-prediction systems, and showed that the set of promoters which are detected by this method is substantially similar to that detected by existing methods.
conclusionsThe results presented here indicate that the promoter signal is the strongest single motif-based signal in the non-coding functional fraction of the genome. They also lend support to the belief that there exists a substantial subset of promoter regions which share several common features including, but not restricted to, a relative abundance of CpG dinucleotides. This subset is detectable by a variety of distinct computational methods.
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Registered trials
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