Abstract
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 language | English (US) |
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Title of host publication | WSOM 2005 - 5th Workshop on Self-Organizing Maps |
Pages | 677-685 |
Number of pages | 9 |
State | Published - 2005 |
Event | 5th Workshop on Self-Organizing Maps, WSOM 2005 - Paris, France Duration: Sep 5 2005 → Sep 8 2005 |
Other
Other | 5th Workshop on Self-Organizing Maps, WSOM 2005 |
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Country/Territory | France |
City | Paris |
Period | 9/5/05 → 9/8/05 |
All Science Journal Classification (ASJC) codes
- Information Systems