期刊
BIOINFORMATICS
卷 33, 期 22, 页码 3575-3583出版社
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btx480
关键词
-
类别
资金
- King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) [URF/1/1976-04, URF/1/3007-01]
- NSF [IIS-1218749, IIS-1639792 EAGER]
- NIH [BIGDATA 1R01GM108341]
- NSF CAREER [IIS-1350983]
- ONR [N00014-15-1-2340]
- NVIDIA
- Intel
- Amazon AWS
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. 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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据