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Cited by 32Machine Learning in BioinformaticsBioinformatics and Genomic NetworksGenomics and Phylogenetic Studies

DeepGOA: Predicting Gene Ontology Annotations of Proteins via Graph Convolutional Network

Zhou, Guangjie, Wang, Jun, Zhang, Xiangliang, Yu, Guoxian·2019·2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
DOI 10.1109/bibm47256.2019.8983075
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Abstract

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.

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  • Characterizing a protein's function in specific biological contexts is challenging and experimentally intensive.

    “Characterizing a protein’s function in specific biological contexts is a challenging and experimentally intensive task [122, 123].”

    From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [123]

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Authors
Zhou, G., Wang, J., Zhang, X., & Yu, G.
Affiliations
  • Southwest University (CN)
  • King Abdullah University of Science and Technology (SA)
ORCID
Venue
2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Publisher
IEEE
Published
2019-11
Pages
1836-1841
DOI
10.1109/bibm47256.2019.8983075
OpenAlex
W3005335133
Cited by
32
References
33
Citations by year
2026: 3 · 2025: 12 · 2024: 2 · 2023: 3 · 2022: 7 · 2021: 3 · 2020: 2
FWCI
9.07
Type
conference paper
Language
en
Open access
closed
Licence
Locations
  • IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (publishedVersion)
  • King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology) (submittedVersion)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
Keywords
protein function prediction, graph convolutional networks, directed acyclic graphs

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