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

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    DeepGOA.pdf
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    Type
    Conference Paper
    Authors
    Zhou, Guangjie
    Wang, Jun
    Zhang, Xiangliang cc
    Yu, Guoxian
    KAUST Department
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Machine Intelligence & kNowledge Engineering Lab
    Date
    2020-02-07
    Permanent link to this record
    http://hdl.handle.net/10754/663486
    
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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.
    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/bibm47256.2019.8983075
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Conference/Event name
    2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
    ISBN
    9781728118673
    DOI
    10.1109/BIBM47256.2019.8983075
    Additional Links
    https://ieeexplore.ieee.org/document/8983075/
    ae974a485f413a2113503eed53cd6c53
    10.1109/BIBM47256.2019.8983075
    Scopus Count
    Collections
    Conference Papers; Computer Science Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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