BACKGROUND As a standardized vocabulary of phenotypic abnormalities associated with human diseases, the Human Phenotype Ontology (HPO) has been widely used by researchers to annotate phenotypes of genes/proteins. For saving the cost and time spent on experiments, many computational approaches have been proposed. They are able to alleviate the problem to some extent, but their performances are still far from satisfactory. METHOD For inferring large-scale protein-phenotype associations, we propose HPOAnnotator that incorporates multiple Protein-Protein Interaction (PPI) information and the hierarchical structure of HPO. Specifically, we use a dual graph to regularize Non-negative Matrix Factorization (NMF) in a way that the information from different sources can be seamlessly integrated. In essence, HPOAnnotator solves the sparsity problem of a protein-phenotype association matrix by using a low-rank approximation. RESULTS By combining the hierarchical structure of HPO and co-annotations of proteins, our model can well capture the HPO semantic similarities. Moreover, graph Laplacian regularizations are imposed in the latent space so as to utilize multiple PPI networks. The performance of HPOAnnotator has been validated under cross-validation and independent test. Experimental results have shown that HPOAnnotator outperforms the competing methods significantly. CONCLUSIONS Through extensive comparisons with the state-of-the-art methods, we conclude that the proposed HPOAnnotator is able to achieve the superior performance as a result of using a low-rank approximation with a graph regularization. It is promising in that our approach can be considered as a starting point to study more efficient matrix factorization-based algorithms.

中文翻译：

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HPOAnnotator：通过具有 HPO 语义相似性和多个 PPI 网络的低秩近似来改进 HPO 注释的大规模预测。

背景人类表型本体（HPO）作为与人类疾病相关的表型异常的标准化词汇表，已被研究人员广泛用于注释基因/蛋白质的表型。为了节省实验成本和时间，人们提出了许多计算方法。他们能够在一定程度上缓解这一问题，但他们的表现还远远不能令人满意。方法为了推断大规模蛋白质表型关联，我们提出了 HPOAnnotator，它整合了多种蛋白质-蛋白质相互作用 (PPI) 信息和 HPO 的层次结构。具体来说，我们使用对偶图来正则化非负矩阵分解（NMF），从而可以无缝集成来自不同来源的信息。本质上，HPOAnnotator 通过使用低秩近似解决了蛋白质表型关联矩阵的稀疏问题。结果通过结合 HPO 的层次结构和蛋白质的联合注释，我们的模型可以很好地捕获 HPO 语义相似性。此外，在潜在空间中施加图拉普拉斯正则化，以便利用多个 PPI 网络。HPOAnnotator 的性能已通过交叉验证和独立测试得到验证。实验结果表明，HPOAnnotator 显着优于竞争方法。结论 通过与最先进的方法进行广泛比较，我们得出的结论是，由于使用低秩近似和图正则化，所提出的 HPOAnnotator 能够实现卓越的性能。这是有希望的，因为我们的方法可以被视为研究更有效的基于矩阵分解的算法的起点。