id: 05945168
dt: a
an: 05945168
au: Lessa, Felipe; Neto, Daniele Martins; Guimarães, Kátia; Brigido, Marcelo;
    Walter, Maria Emilia
ti: Regene: Automatic construction of a multiple component Dirichlet mixture
    priors covariance model to identify non-coding RNA.
so: Chen, Jianer (ed.) et al., Bioinformatics research and applications. 7th
    international symposium, ISBRA 2011, Changsha, China, May 27‒29,
    2011. Proceedings. Berlin: Springer (ISBN 978-3-642-21259-8/pbk).
    Lecture Notes in Computer Science 6674. Lecture Notes in
    Bioinformatics, 380-391 (2011).
py: 2011
pu: Berlin: Springer
la: EN
cc: 
ut: 
ci: 
li: doi:10.1007/978-3-642-21260-4_36
ab: Summary: Non-coding RNA (ncRNA) molecules do not code for proteins, but
    play important regulatory roles in cellular machinery. Recently,
    different computational methods have been proposed to identify and
    classify ncRNAs. In this work, we propose a covariance model with
    multiple Dirichlet mixture priors to identify ncRNAs. We introduce a
    tool, named Regene, to derive these priors automatically from known
    ncRNAs families included in Rfam. Results from experiments with 14
    families improved sensitivity and specificity with respect to single
    component priors.
rv: