Skip NavigationSkip to Content
Return Return to Search Results

RNA secondary structure prediction from sequence alignments using a network of k-nearest neighbor classifiers

  1. Author:
    Bindewald, E.
    Shapiro, B. A.

  2. Author Address

    NCI, Ctr Canc Res, Nanobiol Program, Frederick, MD 21702 USA. SAIC Frederick Inc, Basic Res Program, Frederick, MD USA Shapiro, BA, NCI, Ctr Canc Res, Nanobiol Program, Bldg 469,Room 150, Frederick, MD 21702 USA
    1. Year: 2006
  2. Journal: Rna-a Publication of the Rna Society
    1. 12
    2. 3
    3. Pages: 342-352
  4. Type of Article: Article
  1. Abstract:

    We present a machine learning method (a hierarchical network of k-nearest neighbor classifiers) that uses an RNA sequence alignment in order to predict a consensus RNA secondary structure. The input to the network is the mutual information, the fraction of complementary nucleotides, and a novel consensus RNAfold secondary structure prediction of a pair of alignment columns and its nearest neighbors. Given this input, the network computes a prediction as to whether a particular pair of alignment columns corresponds to a base pair. By using a comprehensive test set of 49 RFAM alignments, the program KNetFold achieves an average Matthews correlation coefficient of 0.81. This is a significant improvement compared with the secondary structure prediction methods PFOLD and RNAalifold. By using the example of archaeal RNase P, we show that the program can also predict pseudoknot interactions

    See More

  1. Keywords:

External Sources

  1. View record in Web of Science®
  2. WOS: 000235793000004

Library Notes

  1. No notes added.
NCI at Frederick

You are leaving a government website.

This external link provides additional information that is consistent with the intended purpose of this site. The government cannot attest to the accuracy of a non-federal site.

Linking to a non-federal site does not constitute an endorsement by this institution or any of its employees of the sponsors or the information and products presented on the site. You will be subject to the destination site's privacy policy when you follow the link.

ContinueCancel