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Exploring Pharmacogenomic LOD for Molecular Explanations of Gene-Drug Relationships

Representative: Adrien Coulet (coulet -- @ --

Gitter room

BioHackathon 2018 Paris/07-PharmaLOD

Hacking task presentation


Through all Biohackathon week (12-16 Nov.)


Collaborators of the ANR PractiKPharma project --


  • Adrien Coulet (all week)
  • Clément Jonquet (Nov. 12-14)
  • Bastien Rance (on Skype)
  • Bousquet Cedric (all week)

Background information

The idea of the hacking task is to find elements of mechanistic/molecular explanations of pharmacogenomic (PGx) drug-gene relationships present in our PGx Linked Data set, named PGxLOD ( Proposing molecular explanation can be seen as predicting novel links between a drug and a target protein, or between a drug and a gene to a same unique pathway. We consider these links as interpretation elements to experts who wonder why a gene impacts a drug response. This may include enriching of our initial linked data, adding provenance elements and experimenting link prediction methods (or others).

Expected outcomes

1/ Enrichment and reuse of our Pharmacogenomic LOD 2/ Getting our Pharmacogenomic LOD more FAIR 3/ Learning from the Elixir network experience 4/ Developing network for future projects at the interface between bioinformatics and medical informatics 5/ Networking for our PhD students and postdocs

Expected audience

Python programmers, RDF fans, FAIR evangelists, AI enthousiasts

Related works and references

Presentation of the PractiKPharma project:

Basic schema for our pharmacogneomics LOD:

Article about knowledge comparison within PGxLOD:


Previous work on link prediction from the LOD (Many thanks to Maulik Kamdar for these links):

Dalleau et al.:

Maulik R. Kamdar, Mark A. Musen: PhLeGrA: Graph Analytics in Pharmacology over the Web of Life Sciences Linked Open Data. WWW 2017: 321-329 Mining the Web of Life Sciences Linked Open Data for Mechanism-Based Pharmacovigilance. WWW (Companion Volume) 2018: 861-865

Munoz et al.: Facilitating prediction of adverse drug reactions by using knowledge graphs and multi-label learning models.

The KB workgroup of OHDSI: Large-scale adverse effects related to treatment evidence standardization (LAERTES): an open scalable system for linking pharmacovigilance evidence sources with clinical data

Ayvaz et al.:Toward a complete dataset of drug–drug interaction information from publicly available sources

Noor et al.: Drug-drug interaction discovery and demystification using Semantic Web technologies

Malec et al.: Using the Literature to Construct Causal Models for Pharmacovigilance

GitHub or any other public repositories of your FOSS products (if any)

To access the data, you can use of the following server

username: practikpharma

password: ask

Only through Loria VPN:


Adrien Coulet Andon Tchechmedjiev Cedric Bousquet Francois-Elie Calvier William Digan Joel Legrand Clement Jonquet Pierre Monnin Malika Smail Tabbone (remote) Bastien Rance (remote) Athenais Vaginay (remote) [to complete]

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