| pubmed-article:19295136 | rdf:type | pubmed:Citation | lld:pubmed |
| pubmed-article:19295136 | lifeskim:mentions | umls-concept:C0020792 | lld:lifeskim |
| pubmed-article:19295136 | lifeskim:mentions | umls-concept:C0017337 | lld:lifeskim |
| pubmed-article:19295136 | lifeskim:mentions | umls-concept:C1101610 | lld:lifeskim |
| pubmed-article:19295136 | lifeskim:mentions | umls-concept:C0336791 | lld:lifeskim |
| pubmed-article:19295136 | lifeskim:mentions | umls-concept:C0521116 | lld:lifeskim |
| pubmed-article:19295136 | lifeskim:mentions | umls-concept:C1521840 | lld:lifeskim |
| pubmed-article:19295136 | pubmed:issue | 8 | lld:pubmed |
| pubmed-article:19295136 | pubmed:dateCreated | 2009-5-7 | lld:pubmed |
| pubmed-article:19295136 | pubmed:abstractText | The discovery of microRNAs (miRNAs), almost 10 years ago, changed dramatically our perspective on eukaryotic gene expression regulation. However, the broad and important functions of these regulators are only now becoming apparent. The expansion of our catalogue of miRNA genes and the identification of the genes they regulate owe much to the development of sophisticated computational tools that have helped either to focus or interpret experimental assays. In this article, we review the methods for miRNA gene finding and target identification that have been proposed in the last few years. We identify some problems that current approaches have not yet been able to overcome and we offer some perspectives on the next generation of computational methods. | lld:pubmed |
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