A structure-based Multiple-Instance Learning approach to predicting in vitro transcription factor-DNA interaction.
BMC Genomics
; 16 Suppl 4: S3, 2015.
Article
em En
| MEDLINE
| ID: mdl-25917392
ABSTRACT
BACKGROUND:
Understanding the mechanism of transcriptional regulation remains an inspiring stage of molecular biology. Recently, in vitro protein-binding microarray experiments have greatly improved the understanding of transcription factor-DNA interaction. We present a method - MIL3D - which predicts in vitro transcription factor binding by multiple-instance learning with structural properties of DNA.RESULTS:
Evaluation on in vitro data of twenty mouse transcription factors shows that our method outperforms a method based on simple-instance learning with DNA structural properties, and the widely used k-mer counting method, for nineteen out of twenty of the transcription factors. Our analysis showed that the MIL3D approach can utilize subtle structural similarities when a strong sequence consensus is not available.CONCLUSION:
Combining multiple-instance learning and structural properties of DNA has promising potential for studying biological regulatory networks.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Fatores de Transcrição
/
DNA
/
Biologia Computacional
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Limite:
Animals
Idioma:
En
Ano de publicação:
2015
Tipo de documento:
Article