DeepGraphGO: graph neural network for large-scale, multispecies protein function prediction.
DeepGraphGO: graph neural network for large-scale, multispecies protein function prediction.
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DeepGraphGO：用于大规模、多物种蛋白质功能预测的图神经网络
DOI：
10.1093/bioinformatics/btab270
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发表时间：
2021-07-12
期刊：
影响因子：
--
通讯作者：
Zhu S
中科院分区：
文献类型：
--
作者：
You R;Yao S;Mamitsuka H;Zhu S
蛋白质自动功能预测（AFP）是一个大规模的多标签分类问题。大多数基于网络的AFP方法的两个局限性是（i）必须为每个物种训练单个模型，以及（ii）完全忽略蛋白质序列信息。这些限制导致性能低于基于序列的方法。因此，挑战是如何开发一个强大的基于网络的方法AFP克服这些局限性。结果我们提出了DeepGraphGO，这是一种端到端的，基于多物种图神经网络的AFP方法，它充分利用了蛋白质序列和高阶蛋白质网络信息。我们的多物种策略允许为所有物种训练一个模型，这表明训练样本的数量比现有方法要多。在大规模数据集上进行的大量实验表明，DeepGraphGO的性能明显优于许多竞争性的最先进的方法，包括DeepGOPlus和三种代表性的基于网络的方法：GeneMANIA，deepNF和clusDCA。我们进一步证实了我们的多物种策略的有效性以及DeepGraphGO相对于所谓的困难蛋白质的优势。最后，我们将DeepGraphGO集成到最先进的集成方法NetGO中，作为一个组件，并实现了进一步的性能改进。可用性和实施https://github.com/yourh/DeepGraphGO。补充信息补充数据可在Bioinformatics在线获得。
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. Availability and implementation https://github.com/yourh/DeepGraphGO. Supplementary information Supplementary data are available at Bioinformatics online.
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影响因子：
14.9
作者：
Mitchell AL;Attwood TK;Babbitt PC;Blum M;Bork P;Bridge A;Brown SD;Chang HY;El-Gebali S;Fraser MI;Gough J;Haft DR;Huang H;Letunic I;Lopez R;Luciani A;Madeira F;Marchler-Bauer A;Mi H;Natale DA;Necci M;Nuka G;Orengo C;Pandurangan AP;Paysan-Lafosse T;Pesseat S;Potter SC;Qureshi MA;Rawlings ND;Redaschi N;Richardson LJ;Rivoire C;Salazar GA;Sangrador-Vegas A;Sigrist CJA;Sillitoe I;Sutton GG;Thanki N;Thomas PD;Tosatto SCE;Yong SY;Finn RD
通讯作者：
Finn RD
影响因子：
5.8
作者：
Gligorijevic, Vladimir;Barot, Meet;Bonneau, Richard
通讯作者：
Bonneau, Richard
影响因子：
12.3
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通讯作者：
Radivojac P
影响因子：
46.9
作者：
Schwikowski, B;Uetz, P;Fields, S
通讯作者：
Fields, S
DOI：
10.1093/bioinformatics/btu031
发表时间：
2014-05-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Jones P;Binns D;Chang HY;Fraser M;Li W;McAnulla C;McWilliam H;Maslen J;Mitchell A;Nuka G;Pesseat S;Quinn AF;Sangrador-Vegas A;Scheremetjew M;Yong SY;Lopez R;Hunter S
通讯作者：
Hunter S