Modeling in-vivo protein-DNA binding by combining multiple-instance learning with a hybrid deep neural network.

ABSTRACT: Modeling in-vivo protein-DNA binding is not only fundamental for further understanding of the regulatory mechanisms, but also a challenging task in computational biology. Deep-learning based methods have succeed in modeling in-vivo protein-DNA binding, but they often (1) follow the fully supervised learning framework and overlook the weakly supervised information of genomic sequences that a bound DNA sequence may has multiple TFBS(s), and, (2) use one-hot encoding to encode DNA sequences and ignore the dependencies among nucleotides. In this paper, we propose a weakly supervised framework, which combines multiple-instance learning with a hybrid deep neural network and uses k-mer encoding to transform DNA sequences, for modeling in-vivo protein-DNA binding. Firstly, this framework segments

SUBMITTER: Zhang Q 

PROVIDER: S-EPMC6559991 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature