TY - JOUR
T1 - KG-Hub - building and exchanging biological knowledge graphs
AU - Caufield, J. Harry
AU - Putman, Tim
AU - Schaper, Kevin
AU - Unni, Deepak R.
AU - Hegde, Harshad
AU - Callahan, Tiffany J.
AU - Cappelletti, Luca
AU - Moxon, Sierra A.T.
AU - Ravanmehr, Vida
AU - Carbon, Seth
AU - Chan, Lauren E.
AU - Cortes, Katherina
AU - Shefchek, Kent A.
AU - Elsarboukh, Glass
AU - Balhoff, Jim
AU - Fontana, Tommaso
AU - Matentzoglu, Nicolas
AU - Bruskiewich, Richard M.
AU - Thessen, Anne E.
AU - Harris, Nomi L.
AU - Munoz-Torres, Monica C.
AU - Haendel, Melissa A.
AU - Robinson, Peter N.
AU - Joachimiak, Marcin P.
AU - Mungall, Christopher J.
AU - Reese, Justin T.
N1 - Publisher Copyright:
© 2023 The Author(s). Published by Oxford University Press.
PY - 2023/7/1
Y1 - 2023/7/1
N2 - Motivation: Knowledge graphs (KGs) are a powerful approach for integrating heterogeneous data and making inferences in biology and many other domains, but a coherent solution for constructing, exchanging, and facilitating the downstream use of KGs is lacking. Results: Here we present KG-Hub, a platform that enables standardized construction, exchange, and reuse of KGs. Features include a simple, modular extract-transform-load pattern for producing graphs compliant with Biolink Model (a high-level data model for standardizing biological data), easy integration of any OBO (Open Biological and Biomedical Ontologies) ontology, cached downloads of upstream data sources, versioned and automatically updated builds with stable URLs, web-browsable storage of KG artifacts on cloud infrastructure, and easy reuse of transformed subgraphs across projects. Current KG-Hub projects span use cases including COVID-19 research, drug repurposing, microbial-environmental interactions, and rare disease research. KG-Hub is equipped with tooling to easily analyze and manipulate KGs. KG-Hub is also tightly integrated with graph machine learning (ML) tools which allow automated graph ML, including node embeddings and training of models for link prediction and node classification.
AB - Motivation: Knowledge graphs (KGs) are a powerful approach for integrating heterogeneous data and making inferences in biology and many other domains, but a coherent solution for constructing, exchanging, and facilitating the downstream use of KGs is lacking. Results: Here we present KG-Hub, a platform that enables standardized construction, exchange, and reuse of KGs. Features include a simple, modular extract-transform-load pattern for producing graphs compliant with Biolink Model (a high-level data model for standardizing biological data), easy integration of any OBO (Open Biological and Biomedical Ontologies) ontology, cached downloads of upstream data sources, versioned and automatically updated builds with stable URLs, web-browsable storage of KG artifacts on cloud infrastructure, and easy reuse of transformed subgraphs across projects. Current KG-Hub projects span use cases including COVID-19 research, drug repurposing, microbial-environmental interactions, and rare disease research. KG-Hub is equipped with tooling to easily analyze and manipulate KGs. KG-Hub is also tightly integrated with graph machine learning (ML) tools which allow automated graph ML, including node embeddings and training of models for link prediction and node classification.
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U2 - 10.1093/bioinformatics/btad418
DO - 10.1093/bioinformatics/btad418
M3 - Article
C2 - 37389415
AN - SCOPUS:85164291000
SN - 1367-4803
VL - 39
JO - Bioinformatics
JF - Bioinformatics
IS - 7
M1 - btad418
ER -