Directed acyclic graph kernels for structural RNA analysis.

ABSTRACT:

Background

Recent discoveries of a large variety of important roles for non-coding RNAs (ncRNAs) have been reported by numerous researchers. In order to analyze ncRNAs by kernel methods including support vector machines, we propose stem kernels as an extension of string kernels for measuring the similarities between two RNA sequences from the viewpoint of secondary structures. However, applying stem kernels directly to large data sets of ncRNAs is impractical due to their computational complexity.

Results

We have developed a new technique based on directed acyclic graphs (DAGs) derived from base-pairing probability matrices of RNA sequences that significantly increases the computation speed of stem kernels. Furthermore, we propose profile-profile stem kernels for multiple al

SUBMITTER: Sato K 

PROVIDER: S-EPMC2515856 | biostudies-literature | 2008 Jul

REPOSITORIES: biostudies-literature