Sombrero: Integrating self-organizing neural networks in the search for dna binding motifs

Shaun Mahony, Panayiotis V. Benos, Terry J. Smith, Aaron Golden

Research output: Contribution to conferencePaperpeer-review


Identification of the short DNA sequence motifs that serve as binding domains for transcription factors continues to be a challenging problem in computational biology. Currently popular methods of motif discovery are based on unsupervised techniques from the statistical learning theory literature. We present here a working prototype of a neural networks based system that aims to tackle the DNA regulatory motif identification problem. The system consists of three modules, the core module being a SOM-based motif-finder named SOMBRERO. The motif-finder is integrated in the prototype with a SOM-based pre-processing method that initialises SOMBRERO with relevant biological knowledge, as well as a self-organizing tree method that helps the user to interpret SOMBRERO's results. The system is demonstrated here using various datasets.

Original languageEnglish (US)
Number of pages9
StatePublished - 2005
Externally publishedYes
Event5th Workshop on Self-Organizing Maps, WSOM 2005 - Paris, France
Duration: Sep 5 2005Sep 8 2005


Other5th Workshop on Self-Organizing Maps, WSOM 2005


  • Motif-finding
  • Self-Organizing Map
  • Self-Organizing Tree
  • Transcription factor binding sites

ASJC Scopus subject areas

  • Information Systems


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