DNA-GCN: Graph convolutional networks for predicting DNA-protein binding

• URL: http://arxiv.org/abs/2106.01836v1
• Date: Wed, 2 Jun 2021 07:36:11 GMT
• Title: DNA-GCN: Graph convolutional networks for predicting DNA-protein binding
• Authors: Yuhang Guo, Xiao Luo, Liang Chen and Minghua Deng
• Abstract summary: We build a sequence k-mer graph and learn DNA Graph Convolutional Network (DNA-GCN) for the whole dataset. DNA-GCN is with a one-hot representation for all nodes, and it then jointly learns the embeddings for both k-mers and sequences. We evaluate our model on 50 datasets from ENCODE.
• Score: 4.1600531290054
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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