Prediction models for DNA transcription termination based on SOM networks
Abstract:
This paper presents two efficient models for predicting transcription termination (TT) in human DNA. A neural network, Self-Organizing Map, was used for finding features from a human polyadenylation (polyA) sites dataset. We derived prediction models related to different polyA signals. A program, "Dragon PolyAtt", for predicting TT regions was designed for the two most frequent polyA sites "AAUAAA" and "AUUAAA". In our tests, Dragon PolyAtt predicts TT regions with a sensitivity of 48.4% (13.6%) and specificity of 74% (79.1%) when searching for polyA signal "AAUAAA" ("AUUAAA"). Both tests were done on human chromosome 21. Results of Dragon PolyAtt system are substantially better than those obtained by the well-known "polyadq" program. © 2005 IEEE.
Año de publicación:
2005
Keywords:
- Bioinfomatics
- Self-Organizing Maps
- Transcription termination
- Polyadenylation sites
Fuente:
scopusTipo de documento:
Conference Object
Estado:
Acceso restringido
Áreas de conocimiento:
- Aprendizaje automático
- Biología molecular
- Ciencias de la computación
Áreas temáticas de Dewey:
- Ciencias de la computación
Procesado con IAObjetivos de Desarrollo Sostenible:
- ODS 3: Salud y bienestar
- ODS 4: Educación de calidad
- ODS 9: Industria, innovación e infraestructura
Procesado con IA