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 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.

PharmGKB, an Integrated Resource of Pharmacogenomic Knowledge

“…At PharmGKB, we have been actively exploring natural language processing (NLP)/text mining approaches to help with the curation process to improve accuracy, coverage, and productivity. We have published multiple papers on identifying and extracting pharmacogenomic concepts and relationships from full text (Coulet, Shah, Garten, Musen, & Altman, 2010; Garten & Altman, 2009; Garten, Coulet, & Altman, 2010; Garten, Tatonetti, & Altman, 2010); the PharmGKB database has also been used repeatedly as the gold standard for evaluation of various text‐mining tools in biomedical research (Guin et al., 2019; Mahmood et al., 2017; Monnin et al., 2019; Pakhomov et al., 2012; Ravikumar, Wagholikar, & Liu, 2014; Yang & Zhao, 2019). More recently, we have developed a supervised machine learning pipeline (PGxMine) to computationally extract possible variant‐drug relationships from abstracts in PubMed or full‐text articles in PubMed Central (Lever et al., 2020).…”

PGxCorpus, a manually annotated corpus for pharmacogenomics

“…In a related work 44 , we used a preliminary, partial and naive set of annotations to test the feasibility of extracting relations and incorporating them in a knowledge network. This included only 307 sentences (out of 945), annotated with a simplified schema of only 4 entity types and 2 relation types.…”

PGxCorpus: a Manually Annotated Corpus for Pharmacogenomics

“…In a related work [32], we used a preliminary, partial and naive set of annotations, for testing the feasibility of extracting relations and incorporating them in a knowledge network. This included only 307 sentences (out of 945), annotated with a simplified schema of only 4 entity types and 2 relation types.…”