期刊
BIOINFORMATICS
卷 37, 期 -, 页码 I262-I271出版社
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btab270
关键词
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类别
资金
- National Natural Science Foundation of China [61872094]
- Shanghai Municipal Science and Technology Major Project [2018SHZDZX01, 2017SHZDZX01]
- ZJ Lab
- Shanghai Center for BrainScience and Brain-Inspired Technology
- 111 Project [B18015]
- Information Technology Facility
- CAS-MPG Partner Institute for Computational Biology
- Shanghai Institute for Biological Sciences, Chinese Academy of Sciences
- Academy of Finland [315896]
- JST ACCEL [JPMJAC1503]
- NEXT KAKENHI [19H04169]
DeepGraphGO is a multispecies graph neural network-based method aimed at solving the problem of automated function prediction of proteins. By utilizing protein sequence and high-order protein network information, a single model can be trained for all species, providing more training samples for AFP. Experimental results demonstrate that DeepGraphGO significantly outperforms other state-of-the-art methods, including network-based GeneMANIA, deepNF, and clusDCA.
Motivation: Automated function prediction (AFP) of proteins is a large-scale multi-label classification problem. Two limitations of most network-based methods for AFP are (i) a single model must be trained for each species and (ii) protein sequence information is totally ignored. These limitations cause weaker performance than sequence-based methods. Thus, the challenge is how to develop a powerful network-based method for AFP to overcome these limitations. Results: We propose DeepGraphGO, an end-to-end, multispecies graph neural network-based method for AFP, which makes the most of both protein sequence and high-order protein network information. Our multispecies strategy allows one single model to be trained for all species, indicating a larger number of training samples than existing methods. Extensive experiments with a large-scale dataset show that DeepGraphGO outperforms a number of competing state-of-the-art methods significantly, including DeepGOPlus and three representative network-based methods: GeneMANIA, deepNF and clusDCA. We further confirm the effectiveness of our multispecies strategy and the advantage of DeepGraphGO over so-called difficult proteins. Finally, we integrate DeepGraphGO into the state-of-the-art ensemble method, NetGO, as a component and achieve a further performance improvement.
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