PhLeGrA: Graph Analytics in Pharmacology over the Web of Life Sciences Linked Open Data

Proceedings of the ... International World-Wide Web Conference
Maulik R Kamdar, Mark A Musen

Abstract

Integrated approaches for pharmacology are required for the mechanism-based predictions of adverse drug reactions that manifest due to concomitant intake of multiple drugs. These approaches require the integration and analysis of biomedical data and knowledge from multiple, heterogeneous sources with varying schemas, entity notations, and formats. To tackle these integrative challenges, the Semantic Web community has published and linked several datasets in the Life Sciences Linked Open Data (LSLOD) cloud using established W3C standards. We present the PhLeGrA platform for Linked Graph Analytics in Pharmacology in this paper. Through query federation, we integrate four sources from the LSLOD cloud and extract a drug-reaction network, composed of distinct entities. We represent this graph as a hidden conditional random field (HCRF), a discriminative latent variable model that is used for structured output predictions. We calculate the underlying probability distributions in the drug-reaction HCRF using the datasets from the U.S. Food and Drug Administration's Adverse Event Reporting System. We predict the occurrence of 146 adverse reactions due to multiple drug intake with an AUROC statistic greater than 0.75. The PhLeGrA platfo...Continue Reading

Citations

Sep 19, 2019·NPJ Digital Medicine·Maulik R KamdarMark A Musen
Dec 18, 2019·Pharmacological Reviews·Jhana O HendrickxStuart Maudsley
May 28, 2021·BMC Medical Informatics and Decision Making·Emmanuel BressoAdrien Coulet

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Software Mentioned

LSLOD
Bio2RDF
CauseNet
SPARQL CONSTRUCT
Translational Ontology Knowledge discovery Engine ( TOKEn
PhLeGrA
SPARQL
HCRF
Tiresias

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