Section: New Results
Scalable methods to query data heterogenity
Participants : Guillaume Alviset, Olivier Dameron, Xavier Garnier, Vijay Ingalalli, Marine Louarn, Yann Rivault, Anne Siegel, Denis Tagu.
Ontology design and integration [O. Dameron, Y. Rivault] We have contribued to several technics improving data integration in onthologies
The ATOL ontology [link to ontology] supports the annotation of phenotype traits in livestock. It was extended with health-related traits. For each organism, livestock diseases are organized according to their type (infectious, genetic, metabolic,...), their transmission and their symptoms. 
queryMed is an R package [url] that provides both high-level and low-level functions for facilitating the integration of reference ontologies and datasets represented in RDF as Linked Data. It currently focuses on drugs indications, interactions and contra-indications by integrating the Drug Indication Database (DID) and the Drug Interaction Knowledge Base (DIKB). Typical applications concern public health and pharmaco-epidemiology. , 
Using AskOmics to integrate heterogeneous data [O. Dameron, A. Siegel]
We contributed to the conversion of an Alzheimer's disease map into a heavyweight ontology, the Alzheimer's Disease Map Ontology (ADMO, [url]), an ontological upper model based on systems biology terms. It provides the ontological formalization for the existing disease map AlzPathway that gives a detailed and broad account of Alzheimer's Disease pathophysiology , .
We also contributed to decipher the role of small non-coding RNAs in the regulation of animal reproduction, especially the role of miR-202 in female fecundity by regulating medaka oncogenesis .
Graph compression and analysis [L. Bourneuf]. We introduced a general approach combining procedural and logical languages to specify graph objects. This is a generalization of previous work , using the reconstruction of Formal Concept Analysis framework example to target the AI community .