DeepGraphGO: Graph neural network for large-scale, multispecies protein function prediction

Ronghui You, Shuwei Yao, Hiroshi Mamitsuka, Shanfeng Zhu*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)
15 Downloads (Pure)

Abstract

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 stateof- the-art ensemble method, NetGO, as a component and achieve a further performance improvement. Availability and implementation: https://github.com/yourh/DeepGraphGO.

Original languageEnglish
Pages (from-to)I262-I271
Number of pages10
JournalBioinformatics
Volume37
DOIs
Publication statusPublished - 1 Jul 2021
MoE publication typeA1 Journal article-refereed

Fingerprint

Dive into the research topics of 'DeepGraphGO: Graph neural network for large-scale, multispecies protein function prediction'. Together they form a unique fingerprint.

Cite this