  Predicting DNA-protein binding is an important and classic problem in
bioinformatics. Convolutional neural networks have outperformed conventional
methods in modeling the sequence specificity of DNA-protein binding. However,
none of the studies has utilized graph convolutional networks for motif
inference. In this work, we propose to use graph convolutional networks for
motif inference. We build a sequence k-mer graph for the whole dataset based on
k-mer co-occurrence and k-mer sequence relationship and then learn DNA Graph
Convolutional Network (DNA-GCN) for the whole dataset. Our DNA-GCN is
initialized with a one-hot representation for all nodes, and it then jointly
learns the embeddings for both k-mers and sequences, as supervised by the known
labels of sequences. We evaluate our model on 50 datasets from ENCODE. DNA-GCN
shows its competitive performance compared with the baseline model. Besides, we
analyze our model and design several different architectures to help fit
different datasets.
