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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Reyes-Herrera, P.H. Acquaviva, A. Ficarra, E. Macii, E. |
| Copyright Year | 2010 |
| Abstract | Gene regulation is one of the most important processes in the molecular biology, in the last years the microRNA molecule, one of the non-coding RNAs involved in the process, has been the focus of attention for several studies. The computational research on this area has gained a notable importance, considering the low amount of experimental information available and the lack of understanding of the microRNA binding mechanism. This article deals with the microRNA-target prediction and presents an innovative method for it. First it generates a set of promising binding sites for a given microRNA using a Genetic Algorithm, at the same time a set of target genes is selected based on the biological process under study. Secondly the set of promising binding sites is mapped into the selected set of target genes, in order to provide real binding sites and finally the resulting targets are filtered according to a biological or structural property. The objectives are to provide a flexible method that is capable of incorporating easily new knowledge, is independent of availability of the experimental information and is able to give hints on the research towards new characteristics among the microRNA binding sites such as motifs. The results present some of this novel properties and present a comparison with the most frequently used methods in the field. |
| Starting Page | 683 |
| Ending Page | 688 |
| File Size | 801774 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424459179 |
| e-ISBN | 9781424459186 |
| DOI | 10.1109/CISIS.2010.129 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-02-15 |
| Publisher Place | Poland |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | microRNA target prediction Sequences Uncertainty RNA Genetic Algorithm Gene expression Genetic algorithms Filters Biological processes motifs Machine learning Software systems Competitive intelligence |
| Content Type | Text |
| Resource Type | Article |
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