Sequence2Vec: a novel embedding approach for modeling transcription factor binding affinity landscape

@article{Dai2017Sequence2VecAN,
  title={Sequence2Vec: a novel embedding approach for modeling transcription factor binding affinity landscape},
  author={Hanjun Dai and Ramzan Umarov and Hiroyuki Kuwahara and Yu Li and Le Song and Xin Gao},
  journal={Bioinformatics},
  year={2017},
  volume={33},
  pages={3575 - 3583}
}
Motivation An accurate characterization of transcription factor (TF)‐DNA affinity landscape is crucial to a quantitative understanding of the molecular mechanisms underpinning endogenous gene regulation. While recent advances in biotechnology have brought the opportunity for building binding affinity prediction methods, the accurate characterization of TF‐DNA binding affinity landscape still remains a challenging problem. Results Here we propose a novel sequence embedding approach for modeling… 

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