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Prediction of Transcription Factor Families Using DNA Sequence Features.
- Source :
- Pattern Recognition in Bioinformatics (9783540884347); 2008, p154-164, 11p
- Publication Year :
- 2008
-
Abstract
- Understanding the mechanisms of protein-DNA interaction is of critical importance in biology. Transcription factor (TF) binding to a specific DNA sequence depends on at least two factors: A protein-level DNA-binding domain and a nucleotide-level specific sequence serving as a TF binding site. TFs have been classified into families based on these factors. TFs within each family bind to specific nucleotide sequences in a very similar fashion. Identification of the TF family that might bind at a particular nucleotide sequence requires a machine learning approach. Here we considered two sets of features based on DNA sequences and their physicochemical properties and applied a one-versus-all SVM (OVA-SVM) with class-wise optimized features to identify TF family-specific features in DNA sequences. Using this approach, a mean prediction accuracy of ~80% was achieved, which represents an improvement of ~7% over previous approaches on the same data. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISBNs :
- 9783540884347
- Database :
- Complementary Index
- Journal :
- Pattern Recognition in Bioinformatics (9783540884347)
- Publication Type :
- Book
- Accession number :
- 76726960
- Full Text :
- https://doi.org/10.1007/978-3-540-88436-1_14