Name
Abstract | ||
|---|---|---|
The use of covariance models in finding non-coding RNA gene members in genome sequence databases has been shown quite effective in many studies. However, it has a significant drawback, which is the very large computational burden. A combined covariance model is proposed to reduce the search complexity when a genome sequence is searched for more than one ncRNA gene family. The covariance models that are combined are selected using a hierarchical clustering algorithm. This study shows that when a small number of original covariance models are combined, the combined covariance model can find members from all original ncRNA families thus successfully reducing the search time. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/CIBCB.2011.5948474 | CIBCB |
Keywords | Field | DocType |
ncrna families,pattern clustering,genomics,noncoding rna gene members,search complexity reduction,covariance models,biology computing,genome sequence databases,macromolecules,noncoding rna gene finding,hierarchical clustering algorithm,formal grammar,sequence analysis,context free grammar,rna,dna sequence,base pair,gene finding,hidden markov models,protein sequence,non coding rna,databases,rna secondary structure,bioinformatics,stem loop,gene family,computational modeling,genome sequence,hierarchical clustering,search algorithm | Hierarchical clustering,Small number,Gene,Computer science,Gene prediction,Genomics,Bioinformatics,Gene family,Non-coding RNA,Covariance | Conference |
ISBN | Citations | PageRank |
978-1-4244-9896-3 | 2 | 0.39 |
References | Authors | |
12 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wenbo Jiang | 1 | 2 | 1.41 |
| Kay C. Wiese | 2 | 164 | 19.10 |