HPODNets: deep graph convolutional networks for predicting human protein-phenotype associations
HPODNets: deep graph convolutional networks for predicting human protein-phenotype associations
复制标题
HPODNets：用于预测人类蛋白质表型关联的深度图卷积网络
DOI：
10.1093/bioinformatics/btab729
复制
发表时间：
2021-10-21
期刊：
影响因子：
5.8
通讯作者：
Zhu, Shanfeng
中科院分区：
文献类型：
--
作者：
Liu, Lizhi;Mamitsuka, Hiroshi;Zhu, Shanfeng
动机：破译人类基因/蛋白与异常表型的关系对疾病的预防、诊断和治疗具有重要意义。人类表型本体（HPO）是描述人类疾病中遇到的表型异常的标准化词汇。然而，目前的HPO注释仍然不完整。因此，有必要计算预测人类蛋白质-表型关联。就目前最前沿的蛋白质注释计算方法（如功能注释）而言，三个重要特征是(i)多网络输入，（ii）半监督学习和（iii）深度图卷积网络（GCN），而预测人类蛋白质的HPO注释没有所有这些特征的方法。结果：我们开发了具有上述三个特征的HPODNets，用于预测人类蛋白质表型关联。HPODNets采用八层深度GCN，可以从多个交互网络中捕获高阶拓扑信息。交叉验证和时间验证的实证结果表明，HPODNets在蛋白质功能预测方面优于7种相互竞争的最先进的方法。具有深度GCNs架构的HPODNets被证实可以有效地预测人类蛋白质的HPO注释，更广泛地说，用于生物信息学中多个生物分子网络输入的节点标签排序问题。
Motivation: Deciphering the relationship between human genes/proteins and abnormal phenotypes is of great importance in the prevention, diagnosis and treatment against diseases. The Human Phenotype Ontology (HPO) is a standardized vocabulary that describes the phenotype abnormalities encountered in human disorders. However, the current HPO annotations are still incomplete. Thus, it is necessary to computationally predict human protein-phenotype associations. In terms of current, cutting-edge computational methods for annotating proteins (such as functional annotation), three important features are (i) multiple network input, (ii) semi-supervised learning and (iii) deep graph convolutional network (GCN), whereas there are no methods with all these features for predicting HPO annotations of human protein.Results: We develop HPODNets with all above three features for predicting human protein-phenotype associations. HPODNets adopts a deep GCN with eight layers which allows to capture high-order topological information from multiple interaction networks. Empirical results with both cross-validation and temporal validation demonstrate that HPODNets outperforms seven competing state-of-the-art methods for protein function prediction. HPODNets with the architecture of deep GCNs is confirmed to be effective for predicting HPO annotations of human protein and, more generally, node label ranking problem with multiple biomolecular networks input in bioinformatics.