Name
Abstract | ||
|---|---|---|
Covariance models provide excellent accuracy for ncRNA homology search. However, high computational complexity has limited their usefulness. This research improves the covariance model's search efficiency by building combined models for a group of different RNA families, which is selected using a clustering strategy. A series of combined partial covariance models are built from the stem loop structural elements that the ncRNA gene families share. Experimental results suggest that for most RNA gene families investigated, our combination search method successfully provides run time improvement with acceptable accuracy. Although there still exist limitations such as recall loss for a few RNA gene families, this novel combination approach has implications for future studies of reducing covariance model's search complexity. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/CIBCB.2012.6217245 | CIBCB |
Keywords | Field | DocType |
pattern clustering,covariance analysis,ncrna gene families,ncrna homology search,ncrna,covariance models,biological techniques,molecular biophysics,stem loop structural elements,noncoding rna gene finding,clustering strategy,combination,bioinformatics,rna,combined partial covariance models,clustering,non coding rna,computer model,gene finding,gene family,computational modeling,computational complexity,stem loop,databases,genomics | RNA,Computer science,Gene prediction,Artificial intelligence,Bioinformatics,Cluster analysis,Analysis of covariance,Non-coding RNA,Machine learning,Stem-loop,Computational complexity theory,Covariance | Conference |
ISBN | Citations | PageRank |
978-1-4673-1190-8 | 0 | 0.34 |
References | Authors | |
11 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wenbo Jiang | 1 | 2 | 1.41 |
| Kay C. Wiese | 2 | 164 | 19.10 |