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

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 th

SUBMITTER: Bindewald E 

PROVIDER: S-EPMC1383574 | biostudies-literature | 2006 Mar

REPOSITORIES: biostudies-literature