Section: Partnerships and Cooperations

International Initiatives

Inria International Labs


Associate Team involved in the International Lab:

  • Title: Discovering knowledge on drug response variability by mining electronic health records

  • International Partner (Institution - Laboratory - Researcher):

    • Stanford (United States) - Department of Medicine, Stanford Center for Biomedical Informatics Research (BMIR) - Nigam Shah

  • Start year: 2017

  • See also: http://snowball.loria.fr/

  • Snowball (2017-2019) is an Inria Associate Team and the continuation of the preceding Associate Team called Snowflake (2014-2016). The objective of Snowball is to study drug response variability through the lens of Electronic Health Records (EHRs) data. This is motivated by the fact that many factors, genetic as well as environmental, imply different responses from people to the same drug. The mining of EHRs can bring substantial elements for understanding and explaining drug response variability.

    Accordingly the objectives of Snowball are to identify in EHR repositories groups of patients which are responding differently to similar treatments, and then to characterize these groups and predict patient drug sensitivity. These objectives are complementary to those of the PractiKPharma ANR project. Moreover, it should be noticed that Adrien Coulet is continuing a two-years sabbatical stay in the lab of Nigam Shah at Stanford University since September 2017 (granted by an “Inria délégation”).

    Participants of the Snowball Associate Team have been awarded with a Grant Seed funded by Stanford University, to pursue their efforts in AI in Medicine. The granted project will particularly focus on the building of fair and equitable predictive models for medicine (see http://medicine.stanford.edu/news/current-news/standard-news/presenceannouncesseedgrantawardees.html).

Informal International Partners: Research Collaboration with HSE Moscow

Participants : Nacira Abbas, Guilherme Alves Da Silva, Miguel Couceiro, Alain Gély, Nyoman Juniarta, Tatiana Makhalova, Amedeo Napoli, Chedy Raïssi, Justine Reynaud.

An on-going collaboration involves the Orpailleur team and Sergei O. Kuznetsov at Higher School of Economics in Moscow (HSE). Amedeo Napoli visited HSE laboratory several times while Sergei O. Kuznetsov visits Inria Nancy Grand Est every year. The collaboration is materialized by the joint supervision of students (such as the thesis of Aleksey Buzmakov defended in 2015 and the on-going thesis of Tatiana Makhalova), and the the organization of scientific events, as the workshop FCA4AI with six editions between 2012 and 2018 (see http://www.fca4ai.hse.ru).

This year, we participated in the writing of common publications around the thesis work of Tatiana Makhalova and the organization of one main event, namely the sixth edition of the FCA4AI workshop in July 2018 at the ECAI-IJCAI Conference which was held in Stockholm, Sweden (see http://ceur-ws.org/Vol-2149, [58]).

Participation in other International Programs

A stay at NASA Frontier Development Lab

In July and August 2018, Chedy Raïssi visited NASA Ames and SETI Institute as part of the Frontier Development Lab, where he worked on mentoring teams and developing meaningful research opportunities, as well as support the work of the planetary defense community and show the potential of this kind of applied research methodology to deliver breakthrough of significant value.

During the eight-week research incubator he aimed at applying cutting-edge machine-learning algorithms to challenges in the space sciences. He worked with two machine-learning students (PhD and post-doc level) that were paired with two space-science researchers (post-doc level) on the improvement of machine-learning models for exoplanet transit classification. This small team started initially from a machine-learning model that classified signals based on straightforward local and global views of the light curves that was developed by Google Brain engineer Chris Shallue. To improve upon it, the team added scientific domain knowledge –staying true to the Orpailleur idea of injecting domain knowledge– that was provided by domain experts. Using the resulting model, the team managed to classify a Kepler data set with 97.5% accuracy and 98% average precision [2].