2018
DOI: 10.1101/390971
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PGxO and PGxLOD: a reconciliation of pharmacogenomic knowledge of various provenances, enabling further comparison

Abstract: Background Pharmacogenomics (PGx) studies how genomic variations impact variations in drug response phenotypes. Knowledge in pharmacogenomics is typically composed of units that have the form of ternary relationships gene variant -drug -adverse event. Such a relationship states that an adverse event may occur for patients having the specified gene variant and being exposed to the specified drug. State-of-the-art knowledge in PGx is mainly available in reference databases such as PharmGKB and reported in scient… Show more

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Cited by 6 publications
(10 citation statements)
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“…We experimented with PGxLOD 2 , a large knowledge graph about pharmacogenomics (PGx) that we previously built [16]. Our approach is implemented in Python, using PyTorch and the Deep Graph Library for learning embeddings, and scikit-learn for clustering.…”
Section: Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…We experimented with PGxLOD 2 , a large knowledge graph about pharmacogenomics (PGx) that we previously built [16]. Our approach is implemented in Python, using PyTorch and the Deep Graph Library for learning embeddings, and scikit-learn for clustering.…”
Section: Methodsmentioning
confidence: 99%
“…We detail the core of our matching approach (node embeddings and clustering) in Section 3, and how inference rules associated with domain knowledge are considered in Section 4. In Section 5, we experiment this approach on PGxLOD, a large knowledge graph we built that contains 50,435 PGx relationships [16]. Finally, we discuss our results and conclude in Section 6 and 7.…”
Section: Introductionmentioning
confidence: 99%
“…We explore PGxLOD 14 [10], a knowledge graph that aggregates several sets of Linked Open Data (LOD) describing drugs, phenotypes, and genetic factors: PharmGKB, ClinVar, DrugBank, SIDER, DisGeNET, and CTD. This aggregation may lead to features combining units from several LOD sets.…”
Section: Optional and Domain-dependent Filteringmentioning
confidence: 99%
“…P h is explained in Subsection 3.3.4 10. Additionally, to avoid loops, P can only be expanded at iteration h with individuals ve such that there exists at least one seed vertex in SUPPORTSET(P ) whose shortest distance to ve is h.11 Not all generated path patterns remain in the dependency structure at the end of an iteration, see Subsections 3.3.3 and 3.3.4.…”
mentioning
confidence: 99%
“…Indeed, RE allows to extract and structure elements of knowledge from natural language texts by identifying and typing the relationships that may be mentioned between named entities (Pawar et al, 2017). The resulting relations can be normalized and assembled in the form of a knowledge graph (KG), which summarizes a domain of knowledge and may serve as a structured intermediate for subsequent tasks of knowledge discovery or knowledge comparison (Monnin et al, 2019). In this work, we explore the task of RE, in the biomedical domain, and from a deep learning perspective.…”
Section: Introductionmentioning
confidence: 99%