Name
Title | ||
|---|---|---|
Deepgraphgo: Graph Neural Network For Large-Scale, Multispecies Protein Function Prediction |
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 state-of-the-art ensemble method, NetGO, as a component and achieve a further performance improvement. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1093/bioinformatics/btab270 | BIOINFORMATICS |
DocType | Volume | Issue |
Conference | 37 | Supplement_1 |
ISSN | Citations | PageRank |
1367-4803 | 1 | 0.36 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ronghui You | 1 | 2 | 1.40 |
| Shuwei Yao | 2 | 3 | 1.06 |
| Hiroshi Mamitsuka | 3 | 973 | 91.71 |
| Shanfeng Zhu | 4 | 429 | 35.04 |