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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 language | English |
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Pages (from-to) | I262-I271 |
Number of pages | 10 |
Journal | Bioinformatics |
Volume | 37 |
DOIs | |
Publication status | Published - 1 Jul 2021 |
MoE publication type | A1 Journal article-refereed |
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Dive into the research topics of 'DeepGraphGO: Graph neural network for large-scale, multispecies protein function prediction'. Together they form a unique fingerprint.Projects
- 1 Finished
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-: Intelligent Crop Production: Data-integrative, Multi-task Learning Meets Crop Simulator
Mamitsuka, H., Nariman Zadeh, H., Strahl, J., Guvenc, B., Ji, S., Rissanen, S., Honkamaa, J., Pöllänen, A., Hiremath, S. & Ojala, F.
01/01/2018 → 31/12/2022
Project: Academy of Finland: Other research funding