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README.md
pgxo.owl
task7_adrien_coulet.pdf

README.md

Exploring Pharmacogenomic LOD for Molecular Explanations of Gene-Drug Relationships

Representative: Adrien Coulet (coulet -- @ -- loria.fr)

Gitter room

BioHackathon 2018 Paris/07-PharmaLOD

Hacking task presentation

https://goo.gl/ms6KER

Length

Through all Biohackathon week (12-16 Nov.)

Community


Collaborators of the ANR PractiKPharma project -- http://practikpharma.loria.fr/

Leads


  • 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 (https://pgxlod.loria.fr/). 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: https://ercim-news.ercim.eu/en104/special/mining-electronic-health-records-to-validate-knowledge-in-pharmacogenomics

Basic schema for our pharmacogneomics LOD: https://peerj.com/preprints/3140/

Article about knowledge comparison within PGxLOD: https://www.biorxiv.org/content/early/2018/11/07/390971

References


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

Dalleau et al.: https://jbiomedsem.biomedcentral.com/articles/10.1186/s13326-017-0125-1

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.
https://academic.oup.com/bib/advance-article/doi/10.1093/bib/bbx099/4085292

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 https://link.springer.com/article/10.1186/s13326-017-0115-3

Ayvaz et al.:Toward a complete dataset of drug–drug interaction information from publicly available sources https://www.sciencedirect.com/science/article/pii/S1532046415000738

Noor et al.: Drug-drug interaction discovery and demystification using Semantic Web technologies https://academic.oup.com/jamia/article-lookup/doi/10.1093/jamia/ocw128

Malec et al.: Using the Literature to Construct Causal Models for Pharmacovigilance https://easychair.org/publications/preprint/X6kk

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 pierre.monnin@loria.fr

Only through Loria VPN:

Hackers


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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