PGxO: A very lite ontology to reconcile pharmacogenomic knowledge units

Monnin, Pierre; Jonquet, Clément; Legrand, Joël; Napoli, Amedeo; Coulet, Adrien

doi:10.7287/peerj.preprints.3140v1

Cited by 3 publications

(4 citation statements)

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“…Instantiating PGxO with knowledge extracted from various sources allows to answer the defined competency questions : we are able to represent PGx relationships extracted either from the state of the art (reference databases or the literature) as well as from EHR+biobank studies. The use of heterogeneous sources for instantiating our ontology improved in several ways the modeling of PGx relationships previously drafted in [16]. Among other things, we enabled the representation of phenotypes as proxies for a specific genotype, such as an enzyme activity.…”

Section: Discussionmentioning

confidence: 99%

“…In the preliminary stage of this work [16], we proposed: (i) a first version of the PGxO ontology able to represent simple pharmacogenomic relationships and their potentially multiple provenances and (ii) a set of rules to reconcile PGx knowledge extracted from or discovered in various sources, i.e. to identify when two relationships refer to the same, or to different knowledge units.…”

Section: Introductionmentioning

confidence: 99%

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PGxO and PGxLOD: a reconciliation of pharmacogenomic knowledge of various provenances, enabling further comparison

et al. 2019

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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 scientific biomedical literature. But, PGx knowledge can also be discovered from clinical data, such as Electronic Health Records (EHRs), and in this case, may either correspond to new knowledge or confirm state-of-the-art knowledge that lacks “clinical counterpart” or validation. For this reason, there is a need for automatic comparison of knowledge units from distinct sources. Results In this article, we propose an approach, based on Semantic Web technologies, to represent and compare PGx knowledge units. To this end, we developed PGxO, a simple ontology that represents PGx knowledge units and their components. Combined with PROV-O, an ontology developed by the W3C to represent provenance information, PGxO enables encoding and associating provenance information to PGx relationships. Additionally, we introduce a set of rules to reconcile PGx knowledge, i.e. to identify when two relationships, potentially expressed using different vocabularies and levels of granularity, refer to the same, or to different knowledge units. We evaluated our ontology and rules by populating PGxO with knowledge units extracted from PharmGKB (2701), the literature (65,720) and from discoveries reported in EHR analysis studies (only 10, manually extracted); and by testing their similarity. We called PGxLOD ( PGx Linked Open Data ) the resulting knowledge base that represents and reconciles knowledge units of those various origins. Conclusions The proposed ontology and reconciliation rules constitute a first step toward a more complete framework for knowledge comparison in PGx. In this direction, the experimental instantiation of PGxO, named PGxLOD, illustrates the ability and difficulties of reconciling various existing knowledge sources. Electronic supplementary material The online version of this article (10.1186/s12859-019-2693-9) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

PGxO and PGxLOD: a reconciliation of pharmacogenomic knowledge of various provenances, enabling further comparison

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…Instantiating PGxO with knowledge extracted from various sources allows to answer the defined competency questions: we are able to represent PGx relationships extracted either from the state of the art (reference databases or the literature) as well as from EHR+biobank studies. The use of heterogeneous sources for instantiating our ontology improved in several ways the modeling of PGx relationships previously drafted in [37]. Among other things, we enabled the representation of phenotypes as proxies for a specific genotype, such as an enzyme activity.…”

Section: Discussionmentioning

confidence: 99%

PGxO and PGxLOD: a reconciliation of pharmacogenomic knowledge of various provenances, enabling further comparison

Monnin

Legrand

Husson

et al. 2018

Preprint

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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 scientific biomedical literature. But, PGx knowledge can also be discovered from clinical data, such as Electronic Health Records (EHRs), and in this case, may either correspond to new knowledge or confirm state-of-the-art knowledge that lacks "clinical counterpart" or validation. For this reason, there is a need for automatic comparison of knowledge units from distinct sources. Results In this article, we propose an approach, based on Semantic Web technologies, to represent and compare PGx knowledge units. To this end, we developed PGxO, a simple ontology that represents PGx knowledge units and their components. Combined with PROV-O, an ontology developed by the W3C to represent provenance information, PGxO enables encoding and associating provenance information to PGx relationships. Additionally, we introduce a set of rules to reconcile PGx knowledge, i.e. to identify when two relationships, potentially expressed using different vocabularies and levels of granularity, refer to the same, or to different knowledge units. We evaluated our ontology and rules by populating PGxO with knowledge units extracted from PharmGKB (2,701), the literature (65,720) and from discoveries reported in EHR analysis studies (only 10, manually extracted); and by testing their similarity. We called PGxLOD (PGx Linked Open Data) the resulting knowledge base that represents and reconciles knowledge units of those various origins. Conclusions The proposed ontology and reconciliation rules constitute a first step toward a more complete framework for knowledge comparison in PGx. In this direction, the experimental instantiation of PGxO, named PGxLOD, illustrates the ability and difficulties of reconciling various existing knowledge sources. BackgroundIn this article, we present a simple ontology named PGxO, developed to reconcile and trace knowledge in pharmacogenomics (PGx). We instantiated this ontology with knowledge of various origins to both 1/23 illustrate the relevance of the ontology and constitute a Linked Open Data (LOD) data set for PGx [5].PGx itself studies how genomics impact individual variations in drug response phenotypes [63]. Knowledge in pharmacogenomics is of particular interest for the implementation of personalized medicine, i.e. a medicine tailoring treatments (chosen drugs and dosages) to every patient, in order to reduce the risk of adverse events. Indeed, best known examples of PGx knowledge already led to the development of clinical guidelines and practices [9] that recommend considering individual genotype when ...

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“…They are also used by K o n t o p o u l o s et al [13] in renewable energy to help no technical user to choose the domestic solar hot water system according to their needs, containing up-to-date information on its components and interrelationships, installation costs, etc. They have played a key role in the data management process from the knowledge representation as demonstrated by The Gene Ontology Consortium [11] passing through the integration of heterogeneous data sources as illustrated by C r u z, X i a o and A d v i s L a b [6] and M o n n i n et al [14], data cleaning by B r ü g g e m a n n [15] and the data publishing and access by B u r a n a r a c h et al [16], ending with big data management by E i n e, J u r i s c h and Q u i n t [17] and access control for cloud data by M i c h a e l, K o t h a n d a r a m a n and K a l i y a n [18].…”

Section: Related Workmentioning

confidence: 99%

The Implementation of Credit Risk Scorecard Using Ontology Design Patterns and BCBS 239

Elhassouni

Qadi

Alami

et al. 2020

Cybernetics and Information Technologies

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AbstractNowadays information and communication technologies are playing a decisive role in helping the financial institutions to deal with the management of credit risk. There have been significant advances in scorecard model for credit risk management. Practitioners and policy makers have invested in implementing and exploring a variety of new models individually. Coordinating and sharing information groups, however, achieved less progress. One of several causes of the 2008 financial crisis was in data architecture and information technology infrastructure. To remedy this problem the Basel Committee on Banking Supervision (BCBS) outlined a set of principles called BCBS 239. Using Ontology Design Patterns (ODPs) and BCBS 239, credit risk scorecard and applicant ontologies are proposed to improve the decision making process in credit loan. Both ontologies were validated, distributed in Ontology Web Language (OWL) files and checked in the test cases using SPARQL. Thus, making their (re)usability and expandability easier in financial institutions. These ontologies will also make sharing data more effective and less costly.

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PGxO: A very lite ontology to reconcile pharmacogenomic knowledge units

Cited by 3 publications

References 8 publications

PGxO and PGxLOD: a reconciliation of pharmacogenomic knowledge of various provenances, enabling further comparison

PGxO and PGxLOD: a reconciliation of pharmacogenomic knowledge of various provenances, enabling further comparison

PGxO and PGxLOD: a reconciliation of pharmacogenomic knowledge of various provenances, enabling further comparison

The Implementation of Credit Risk Scorecard Using Ontology Design Patterns and BCBS 239

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