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dc.contributor.authorZhou, Guangjie
dc.contributor.authorWang, Jun
dc.contributor.authorZhang, Xiangliang
dc.contributor.authorGuo, Maozu
dc.contributor.authorYu, Guoxian
dc.date.accessioned2020-12-20T12:41:54Z
dc.date.available2020-12-20T12:41:54Z
dc.date.issued2020-12-16
dc.date.submitted2020-08-20
dc.identifier.citationZhou, G., Wang, J., Zhang, X., Guo, M., & Yu, G. (2020). Predicting functions of maize proteins using graph convolutional network. BMC Bioinformatics, 21(S16). doi:10.1186/s12859-020-03745-6
dc.identifier.issn1471-2105
dc.identifier.doi10.1186/s12859-020-03745-6
dc.identifier.urihttp://hdl.handle.net/10754/666515
dc.description.abstractAbstract Background Maize (Zea mays ssp. mays L.) is the most widely grown and yield crop in the world, as well as an important model organism for fundamental research of the function of genes. The functions of Maize proteins are annotated using the Gene Ontology (GO), which has more than 40000 terms and organizes GO terms in a direct acyclic graph (DAG). It is a huge challenge to accurately annotate relevant GO terms to a Maize protein from such a large number of candidate GO terms. Some deep learning models have been proposed to predict the protein function, but the effectiveness of these approaches is unsatisfactory. One major reason is that they inadequately utilize the GO hierarchy. Results 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 quantifies the correlations (or edges) between GO terms and updates the edge weights of the DAG by leveraging GO annotations and hierarchy, then learns the semantic representation and latent inter-relations of GO terms in the way by applying GCN on the updated DAG. Meanwhile, Convolutional Neural Network (CNN) is used to learn the feature representation of amino acid sequences with respect to the semantic representations. After that, DeepGOA computes the dot product of the two representations, which enable to train the whole network end-to-end coherently. Extensive experiments show that DeepGOA can effectively integrate GO structural information and amino acid information, and then annotates proteins accurately. Conclusions Experiments on Maize PH207 inbred line and Human protein sequence dataset 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. Codes and datasets are available at http://mlda.swu.edu.cn/codes.php?name=DeepGOA.
dc.description.sponsorshipPublication cost is funded by Natural Science Foundation of China (61872300). None of the funding bodies have played any part in the design of the study, in the collection, analysis, and interpretation of the data, or in the writing of the manuscript.
dc.publisherSpringer Nature
dc.relation.urlhttps://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-03745-6
dc.rightsThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.titlePredicting functions of maize proteins using graph convolutional network
dc.typeArticle
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalBMC Bioinformatics
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionSchool of Software, Shandong University, Jinan, China.
dc.contributor.institutionCollege of Computer and Information Sciences, Chongqing, China.
dc.contributor.institutionSchool of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing, China.
dc.identifier.volume21
dc.identifier.issueS16
kaust.personZhang, Xiangliang
kaust.personYu, Guoxian
dc.date.accepted2020-09-08
refterms.dateFOA2020-12-20T12:43:06Z


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This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
Except where otherwise noted, this item's license is described as This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.