TY - JOUR
T1 - The scalable precision medicine open knowledge engine (SPOKE)
T2 - a massive knowledge graph of biomedical information
AU - Morris, John H.
AU - Soman, Karthik
AU - Akbas, Rabia E.
AU - Zhou, Xiaoyuan
AU - Smith, Brett
AU - Meng, Elaine C.
AU - Huang, Conrad C.
AU - Cerono, Gabriel
AU - Schenk, Gundolf
AU - Rizk-Jackson, Angela
AU - Harroud, Adil
AU - Sanders, Lauren
AU - Costes, Sylvain V.
AU - Bharat, Krish
AU - Chakraborty, Arjun
AU - Pico, Alexander R.
AU - Mardirossian, Taline
AU - Keiser, Michael
AU - Tang, Alice
AU - Hardi, Josef
AU - Shi, Yongmei
AU - Musen, Mark
AU - Israni, Sharat
AU - Huang, Sui
AU - Rose, Peter W.
AU - Nelson, Charlotte A.
AU - Baranzini, Sergio E.
N1 - Publisher Copyright:
© The Author(s) 2023. Published by Oxford University Press.
PY - 2023/2/1
Y1 - 2023/2/1
N2 - Motivation: Knowledge graphs (KGs) are being adopted in industry, commerce and academia. Biomedical KG presents a challenge due to the complexity, size and heterogeneity of the underlying information. Results: In this work, we present the Scalable Precision Medicine Open Knowledge Engine (SPOKE), a biomedical KG connecting millions of concepts via semantically meaningful relationships. SPOKE contains 27 million nodes of 21 different types and 53 million edges of 55 types downloaded from 41 databases. The graph is built on the framework of 11 ontologies that maintain its structure, enable mappings and facilitate navigation. SPOKE is built weekly by python scripts which download each resource, check for integrity and completeness, and then create a ‘parent table’ of nodes and edges. Graph queries are translated by a REST API and users can submit searches directly via an API or a graphical user interface. Conclusions/Significance: SPOKE enables the integration of seemingly disparate information to support precision medicine efforts.
AB - Motivation: Knowledge graphs (KGs) are being adopted in industry, commerce and academia. Biomedical KG presents a challenge due to the complexity, size and heterogeneity of the underlying information. Results: In this work, we present the Scalable Precision Medicine Open Knowledge Engine (SPOKE), a biomedical KG connecting millions of concepts via semantically meaningful relationships. SPOKE contains 27 million nodes of 21 different types and 53 million edges of 55 types downloaded from 41 databases. The graph is built on the framework of 11 ontologies that maintain its structure, enable mappings and facilitate navigation. SPOKE is built weekly by python scripts which download each resource, check for integrity and completeness, and then create a ‘parent table’ of nodes and edges. Graph queries are translated by a REST API and users can submit searches directly via an API or a graphical user interface. Conclusions/Significance: SPOKE enables the integration of seemingly disparate information to support precision medicine efforts.
UR - https://www.scopus.com/pages/publications/85148479916
U2 - 10.1093/bioinformatics/btad080
DO - 10.1093/bioinformatics/btad080
M3 - Article
C2 - 36759942
AN - SCOPUS:85148479916
SN - 1367-4803
VL - 39
JO - Bioinformatics
JF - Bioinformatics
IS - 2
M1 - btad080
ER -