| Chapter title |
Computational Prediction of microRNA Targets
|
|---|---|
| Chapter number | 12 |
| Book title |
microRNA: Basic Science
|
| Published in |
Advances in experimental medicine and biology, January 2015
|
| DOI | 10.1007/978-3-319-22380-3_12 |
| Pubmed ID | |
| Book ISBNs |
978-3-31-922379-7, 978-3-31-922380-3
|
| Authors |
Laganà, Alessandro, Alessandro Laganà |
| Abstract |
Computational prediction of microRNA (miRNA) targets is a fundamental step towards the characterization of miRNA function and the understanding of their role in disease. A single miRNA can regulate hundreds of different gene transcripts through partial sequence complementarity and a single gene may be regulated by several miRNAs acting cooperatively. The remarkable advances made in recent years have allowed the identification of key features for functional miRNA binding sites. A plethora of prediction tools are now available, but their accuracies remain rather poor, as miRNA target recognition has revealed itself to be a very complex and dynamic mechanism, still only partially understood.In this chapter, the principles of miRNA target prediction in animals are presented, together with the most up-to-date and effective computational approaches and tools available. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 50% |
| Scientists | 1 | 50% |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Luxembourg | 1 | 6% |
| Unknown | 16 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 6 | 35% |
| Researcher | 4 | 24% |
| Other | 2 | 12% |
| Student > Master | 2 | 12% |
| Professor > Associate Professor | 2 | 12% |
| Other | 0 | 0% |
| Unknown | 1 | 6% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 6 | 35% |
| Biochemistry, Genetics and Molecular Biology | 5 | 29% |
| Computer Science | 2 | 12% |
| Immunology and Microbiology | 1 | 6% |
| Chemistry | 1 | 6% |
| Other | 1 | 6% |
| Unknown | 1 | 6% |