A structure-based Multiple-Instance Learning approach to predicting in vitro transcription factor-DNA interaction.
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.


CONCLUSIONS
Combining multiple-instance learning and structural properties of DNA has promising potential for studying biological regulatory networks.
Zhen
Z
Gao
Jianhua
J
Ruan
D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D004247:DNA / Q000737:chemistry* / Q000378:metabolism*; D066298:In Vitro Techniques; D051379:Mice; D011485:Protein Binding; D014157:Transcription Factors / Q000737:chemistry / Q000378:metabolism*
D014157:Transcription Factors; D004247:DNA