Self-organizing maps of position weight matrices for motif discovery in biological sequences

Shaun Mahony, David Hendrix, Terry J. Smith, Aaron Golden

Research output: Contribution to journalArticle

11 Scopus citations

Abstract

The identification of overrepresented motifs in a collection of biological sequences continues to be a relevant and challenging problem in computational biology. Currently popular methods of motif discovery are based on statistical learning theory. In this paper, a machine-learning approach to the motif discovery problem is explored. The approach is based on a Self-Organizing Map (SOM) where the output layer neuron weight vectors are replaced by position weight matrices. This approach can be used to characterise features present in a set of sequences, and thus can be used as an aid in overrepresented motif discovery. The SOM approach to motif discovery is demonstrated using biological sequence datasets, both real and simulated.

Original languageEnglish (US)
Pages (from-to)397-413
Number of pages17
JournalArtificial Intelligence Review
Volume24
Issue number3-4
DOIs
StatePublished - Nov 1 2005

Keywords

  • Biological motif discovery
  • Self-organizing map

ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language
  • Artificial Intelligence

Fingerprint Dive into the research topics of 'Self-organizing maps of position weight matrices for motif discovery in biological sequences'. Together they form a unique fingerprint.

Cite this