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- Title
HPOFiller: identifying missing protein–phenotype associations by graph convolutional network.
- Authors
Liu, Lizhi; Mamitsuka, Hiroshi; Zhu, Shanfeng
- Abstract
Motivation Exploring the relationship between human proteins and abnormal phenotypes is of great importance in the prevention, diagnosis and treatment of diseases. The human phenotype ontology (HPO) is a standardized vocabulary that describes the phenotype abnormalities encountered in human diseases. However, the current HPO annotations of proteins are not complete. Thus, it is important to identify missing protein–phenotype associations. Results We propose HPOFiller, a graph convolutional network (GCN)-based approach, for predicting missing HPO annotations. HPOFiller has two key GCN components for capturing embeddings from complex network structures: (i) S-GCN for both protein–protein interaction network and HPO semantic similarity network to utilize network weights; (ii) Bi-GCN for the protein–phenotype bipartite graph to conduct message passing between proteins and phenotypes. The core idea of HPOFiller is to repeat run these two GCN modules consecutively over the three networks, to refine the embeddings. Empirical results of extremely stringent evaluation avoiding potential information leakage including cross-validation and temporal validation demonstrates that HPOFiller significantly outperforms all other state-of-the-art methods. In particular, the ablation study shows that batch normalization contributes the most to the performance. The further examination offers literature evidence for highly ranked predictions. Finally using known disease-HPO term associations, HPOFiller could suggest promising, unknown disease–gene associations, presenting possible genetic causes of human disorders. Availabilityand implementation https://github.com/liulizhi1996/HPOFiller. Supplementary information Supplementary data are available at Bioinformatics online.
- Subjects
PROTEIN-protein interactions; HUMAN phenotype; BIPARTITE graphs; MESSAGE passing (Computer science); HUMAN abnormalities; THERAPEUTICS; PHENOTYPES
- Publication
Bioinformatics, 2021, Vol 37, Issue 19, p3328
- ISSN
1367-4803
- Publication type
Article
- DOI
10.1093/bioinformatics/btab224