Sequence2Vec: a novel embedding approach for modeling transcription factor binding affinity landscape.
Bioinformatics (Oxford, England)(2017)
摘要
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 the transcription factor binding affinity landscape. Our method represents DNA binding sequences as a hidden Markov model which captures both position specific information and long-range dependency in the sequence. A cornerstone of our method is a novel message passing-like embedding algorithm, called Sequence2Vec, which maps these hidden Markov models into a common nonlinear feature space and uses these embedded features to build a predictive model. Our method is a novel combination of the strength of probabilistic graphical models, feature space embedding and deep learning. We conducted comprehensive experiments on over 90 large-scale TF-DNA datasets which were measured by different high-throughput experimental technologies. Sequence2Vec outperforms alternative machine learning methods as well as the state-of-the-art binding affinity prediction methods.
AVAILABILITY AND IMPLEMENTATION:Our program is freely available at https://github.com/ramzan1990/sequence2vec.
CONTACT:xin.gao@kaust.edu.sa or lsong@cc.gatech.edu.
SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络