Inria NGE publications

  • Kevin Dalleau, Yassine Marzougui, Sébastien Da Silva, Patrice Ringot, Ndeye Coumba Ndiaye, and Adrien Coulet. Learning from biomedical linked data to suggest valid pharmacogenes. Journal of Biomedical Semantics, 75, 2017. hal-01511773.
  • Joël Legrand, Yannick Toussaint, Chedy Raïssi, and Adrien Coulet. Tree-LSTM and Cross-Corpus Training for Extracting Biomedical Relationships from Text. DLPM2017 Workshop – 2nd International Workshop on Deep Learning for Precision Medicine, September 2017.
  • Pierre Monnin, Clement Jonquet, Joël Legrand, Amedeo Napoli, and Adrien Coulet. PGxO: A very lite ontology to reconcile pharmacogenomic knowledge units. In Methods, tools & platforms for Personalized Medicine in the Big Data Era, NETTAB 2017 Workshop Collection, Palermo, Italy, October 2017.
  • Dalleau K., Ndiaye N.C., Coulet A.: Suggesting Valid Pharmacogenes by Mining Linked Data. In Semantic Web Application and Tools for Life Science Conference 2015.
  • Hassan M., Makkaoui O., Coulet A. and Toussain Y.: Extracting Disease-Symptom Relationships by Learning Syntactic Patterns from Dependency Graphs. In BioNLP 2015, 71-80, ACL.
  • Personeni G., Daget S., Bonnet C., Jonveaux P., Devignes M.-D., Smail-Tabbone M. and Coulet A.: ILP for Mining Linked Open Data. In International Conference on Induced Logic Programming 2014. Short Paper.
  • Personeni G., Daget S., Bonnet C., Jonveaux P., Devignes M.-D., Smail-Tabbone M. and Coulet A.: Mining Linked Open Data: a Case Study with Genes Responsible for Intellectual Disability. In International Conference on Data Integration in Life Sciences 2014, Lecture Notes in Bioinformatics, 8574: 16-31, Springer.
  • Hassan M., Coulet A. and Toussaint Y.: Learning Subgraph Patterns from text for Extracting Disease – Symptom Relationships. In ECML/PKDD Workshop on Interactions between Data Mining and Natural Language Processing, CEUR-WS, Vol.1202:81-96 2014.

Stanford publications

  • Quinn KJ, Shah NH. A dataset quantifying polypharmacy in the United States. Sci Data. 2017
  • Banda JM, Halpern Y, Sontag D, Shah NH. Electronic phenotyping with APHRODITE and the Observational Health Sciences and Informatics (OHDSI) data network. AMIA Jt Summits Transl Sci Proc. 2017
  • Banda J.M., Callahan A., Winnenburg R., Strasberg H.R., Cami A., Reis B.Y., Vilar S., Hripcsak G., Dumontier M., Shah N.H.: Feasibility of Prioritizing Drug–Drug-Event Associations Found in Electronic Health RecordsDrug safety, 2015
  • Odgers D.J., Dumontier M.: Mining Electronic Health Records using Linked Data. AMIA Summits on Translational Science Proceedings, 2015
  • Dumontier, M., Baker, C. J., Baran, J., Callahan, A., Chepelev, L., Cruz-Toledo, J., Del Rio, N. R., Duck, G., Furlong, L. I., Keath, N., Klassen, D., McCusker, J. P., Queralt-Rosinach, N., Samwald, M., Villanueva-Rosales, N., Wilkinson, M. D., Hoehndorf, R. The Semanticscience Integrated Ontology (SIO) for biomedical research and knowledge discovery. Journal of Biomedical Semantics 2014; 5 (1): 14.
  • Iyer, S., Harpaz, R., LePendu, P., Bauer-Mehren, A., Shah, N. Mining Clinical Text for Signals of Adverse Drug-Drug Interactions. JAMIA. 2014; 21 (2): 353-362.
  • Harpaz R, Callahan A, Tamang S, Low Y, Odgers D, Finlayson S, Jung K, LePendu P, Shah NH. Text mining for adverse drug events: the promise, challenges, and state of the art. Drug Saf. 2014 Oct;37(10):777-90.

Common publications


  • Adrien Coulet obtained a délégation, funded by the CNRS, which allow him to spend a the 2017-2018 year at Stanford.
  • Particpants in the Snowflake team have been awarded by the ANR (the French National Research Agency), in the 2015 generic call that will found the PractiKPharma project. The scope of PractiKPharma is complementary with Snowflake, enabling the initiation of many novel interactions.
  • G. Personeni (Inria NGE) obtained the Visiting Student Researcher Fellowship from the France-Stanford Center for Interdisciplinary Studies for 2015-16. He visited Stanford University Nov. 2015-Feb. 2016.
  • Y. Low (Stanford) and A. Coulet (Inria NGE) won the Best Application Prize at the 2014 NCBO Hackathon