Name
Abstract | ||
|---|---|---|
A group of miRNAs can regulate a biological process by targeting genes involved in the process. The unbiased miRNA functional enrichment analysis is the most precise in silico approach to predict the biological processes that may be regulated by a given miRNA group. However, it is computationally intensive and significantly more expensive than its alternatives.We introduce BUFET, a new approach to significantly reduce the time required for the execution of the unbiased miRNA functional enrichment analysis. It derives its strength from the utilization of efficient bitset-based methods and parallel computation techniques.BUFET outperforms the state-of-the-art implementation, in regard to computational efficiency, in all scenarios (both single- and multi-core), being, in some cases, more than one order of magnitude faster. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1186/s12859-017-1812-8 | BMC Bioinformatics |
Keywords | Field | DocType |
BUFET,Functional enrichment analysis,miRNAs | Biology,microRNA,Boosting (machine learning),Computational biology,Bioinformatics,DNA microarray,In silico | Journal |
Volume | Issue | ISSN |
18 | 1 | 1471-2105 |
Citations | PageRank | References |
1 | 0.38 | 5 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Konstantinos Zagganas | 1 | 32 | 2.99 |
| Thanasis Vergoulis | 2 | 364 | 31.81 |
| Maria D. Paraskevopoulou | 3 | 173 | 10.23 |
| Ioannis S. Vlachos | 4 | 298 | 16.27 |
| Spiros Skiadopoulos | 5 | 1139 | 65.60 |
| Theodore Dalamagas | 6 | 792 | 61.84 |