
pmid: 17123865
MicroRNAs (miRNAs) are one class of newly identified riboregulators of gene expression in many eukaryotic organisms. They play important roles in multiple biological and metabolic processes, including developmental timing, signal transduction, cell maintenance and differentiation, diseases and cancers. miRNAs regulate gene expression at the posttranscriptional level by directly cleaving targeted mRNAs or repressing translation. Although the founding members of miRNAs were discovered by genetic screening approaches, experimental approaches were limited by their low efficiency, time consuming, and high cost. As an alternative, computational approaches were developed. Computational approaches for identifying miRNAs are based on the following major characteristics of miRNAs: hairpin-shaped secondary structures, high conservation for some miRNAs, and high minimal folding free energy index (MFEI). Computational approaches also play an important role in identifying miRNA targets. A majority of known miRNAs and their targets were identified by computational approaches. Several web-based or non-web-based computer software programs are publicly available for predicting miRNAs and their targets.
Databases, Factual, Biochemistry, molecular biology, Computational Biology, Gene regulation, MFEI, MicroRNAs, Gene Expression Regulation, Minimal folding free energy index, Gene Targeting, Animals, Humans, Computational approach, Computational methods for problems pertaining to biology, Phylogeny, miRNA
Databases, Factual, Biochemistry, molecular biology, Computational Biology, Gene regulation, MFEI, MicroRNAs, Gene Expression Regulation, Minimal folding free energy index, Gene Targeting, Animals, Humans, Computational approach, Computational methods for problems pertaining to biology, Phylogeny, miRNA
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 155 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 1% |
