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Overall Objectives
Research Program
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Section: Partnerships and Cooperations

International Initiatives

Inria International Labs:SNOWFLAKE

Participants : Adrien Coulet [contact person] , Malika Smaïl-Tabbone.

Inria@SiliconValley

Associate Team involved in the International Lab: SNOWFLAKE

Snowflake (http://snowflake.loria.fr/ ) is an Inria Associate Team which started in 2014. It is aimed at facilitating the collaboration between researchers from the Inria Orpailleur team and the Stanford Center for Biomedical Informatics Research, Stanford University, USA. The main objective of Snowflake is to improve biomedical knowledge discovery by connecting Electronic Health Records (EHRs) with LOD (Linked Open Data). Such a connection would help to complete domain knowledge w.r.t. EHRs. The initial focus of Snowflake is the identification and characterization of groups of patients w.r.t. (adverse) reactions to drugs. Identified features associated with such groups of patients could be used as predictors of over- or under-reactions to some drugs. The considered use case is related to pharmacogenomics drugs, i.e., drugs known to cause variable effects depending on the genetic profile of patients. Data associated with pharmacogenomics drugs and their mechanisms are available in LOD and, once connected to EHRs, they can be used to classify drugs and then patients showing a specific reaction profile to a given group of drugs.

Participation In other International Programs: Ciência Sem Fronteiras

Participant : Amedeo Napoli [contact person] .

Program “Ciência Sem Fronteiras” is a Brazilian research fellowship which provides a funding for the stay of a visiting French researcher in Brazil at Universidade Federal Pernambuco Recife for three years. The on-going project is called “Formal Concept Analysis as a Support for Knowledge Discovery” and is aimed at combining FCA methods with numerical clustering methods used by Brazilian colleagues. This project is supervised in Brazil by Professor Francisco de A.T. de Carvalho (CIn/UFPE).

The project aims at developing and comparing classification and clustering algorithms for complex data (especially interval and multi-valued data). Two families of algorithms are studied, namely “clustering algorithms” based on the use of a similarity or a distance for comparing the objects, and “classification algorithms in Formal Concept Analysis (FCA)” based on attribute sharing between objects. The objectives here are to combine the facilities of both families of algorithms for improving the potential of each family in dealing with more complex and voluminous datasets.

STIC AmSud: Autonomic Knowledge Discovery (AKD)

Participants : Victor Codocedo, Amedeo Napoli [contact person] .

This research project involves researchers with different specialties, from Brazil (Universidade Federal Rio Grande do Sul), from Chile (UFSM Santiago and Valparaiso), from Uruguay (Universidad de la Repùblica), and the Orpailleur Team. The projects targets the design of solutions able to proactively understand the behavior of systems and networks in order to prevent vulnerable states. Accordingly, we aim at integrating knowledge discovery techniques within autonomic systems in order to provide intelligent self-configuration and self-protection mechanisms. The results of this project may not only benefit to end-users but also highly contribute to the scientific community by providing solid foundations for the development of more secure, scalable, and reliable management approaches.

Miscellaneous

Participants : Mehwish Alam, Aleksey Buzmakov, Victor Codocedo, Adrien Coulet, Amedeo Napoli [contact person] , Chedy Raïssi, Jean-Sébastien Sereni, Mario Valencia.