Modeling transcription termination of selected gene groups using support vector machine
Abstract:
In this work we use support vector machine to pbkp_redict polyadenylation sites (Poly (A) sites) in human DNA and mRNA sequences by analyzing features around them. Two models are created. The first model identifies the possible location of the Poly (A) site effectively. The second model distinguishes between true and false Poly (A) sites, hence effectively detect the region where Poly (A) sites and transcription termination occurs. The support vector machine (SVM) approach achieves almost 90% sensitivity, 83% accuracy, 80% precision and 76% specificity on tests of the chromosomal data such as chromosome 21, The models are able to make on average just about one false pbkp_rediction every 7000 nucleotides. In most cases, better results can be achieved in comparison with those reported previously on the same data sets. © 2008 IEEE.
Año de publicación:
2008
Keywords:
Fuente:
Tipo de documento:
Conference Object
Estado:
Acceso restringido
Áreas de conocimiento:
- Expresión génica
- Aprendizaje automático
- Algoritmo
Áreas temáticas:
- Ciencias de la computación