DeepGOA: Predicting Gene Ontology Annotations of Proteins via Graph Convolutional Network
Type
Conference PaperKAUST Department
Computer Science ProgramComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Machine Intelligence & kNowledge Engineering Lab
Date
2020-02-07Permanent link to this record
http://hdl.handle.net/10754/663486
Metadata
Show full item recordAbstract
Gene Ontology (GO) uses a series of standardized and controlled GO terms to describe the molecular functions, biological process roles and cellular locations of gene products (i.e., proteins and RNAs), it structurally organizes GO terms in a direct acyclic graph (DAG). GO has more than 40000 terms and each protein is only annotated with several or dozens of these terms. It is a difficult challenge to accurately annotate relevant GO terms to a protein from such a large number of candidate GO terms. Some deep learning models have been proposed to utilize the GO hierarchy for protein function prediction, but they inadequately utilize GO hierarchy. To use the knowledge encoded in the GO hierarchy, we propose a deep Graph Convolutional Network (GCN) based model (DeepGOA) to predict GO annotations of proteins. DeepGOA firstly utilizes GO annotations and hierarchy to measure the correlations between GO terms and to accordingly update the edge weights of the DAG, and then applies GCN on the updated DAG to learn the semantic representation and latent inter-relations of GO terms. At the same time, it uses Convolutional Neural Network (CNN) to learn the feature representation of amino acids sequences with respect to the semantic representations. After that, DeepGOA computes the dot product of two representations, which enables training the whole network end-to-end in a coherent fashion. Experiments on two model species (Human and Corn) show that DeepGOA outperforms the state-of-the-art deep learning based methods. The ablation study proves that GCN can employ the knowledge of GO and boost the performance.Citation
Zhou, G., Wang, J., Zhang, X., & Yu, G. (2019). DeepGOA: Predicting Gene Ontology Annotations of Proteins via Graph Convolutional Network. 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). doi:10.1109/bibm47256.2019.8983075Conference/Event name
2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019ISBN
9781728118673Additional Links
https://ieeexplore.ieee.org/document/8983075/ae974a485f413a2113503eed53cd6c53
10.1109/BIBM47256.2019.8983075