HPODNets: deep graph convolutional networks for predicting human protein–phenotype associations

Lizhi Liu, Hiroshi Mamitsuka, Shanfeng Zhu

Tutkimustuotos: LehtiartikkeliArticleScientificvertaisarvioitu

4 Sitaatiot (Scopus)
95 Lataukset (Pure)

Abstrakti

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.

AlkuperäiskieliEnglanti
Sivut799–808
Sivumäärä10
JulkaisuBioinformatics
Vuosikerta38
Numero3
Varhainen verkossa julkaisun päivämäärä21 lokak. 2021
DOI - pysyväislinkit
TilaJulkaistu - 1 helmik. 2022
OKM-julkaisutyyppiA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä

Sormenjälki

Sukella tutkimusaiheisiin 'HPODNets: deep graph convolutional networks for predicting human protein–phenotype associations'. Ne muodostavat yhdessä ainutlaatuisen sormenjäljen.

Siteeraa tätä