Section: Bilateral Contracts and Grants with Industry

The BioIntelligence Project

Participants : Mehwish Alam, Aleksey Buzmakov, Melisachew Chekol, Adrien Coulet, Marie-Dominique Devignes, Amedeo Napoli [contact person] , Nicolas Pépin-Hermann, Malika Smaïl-Tabbone.

The objective of the “BioIntelligence” project is to design an integrated framework for the discovery and the development of new biological products. This framework takes into account all phases of the development of a product, from molecular to industrial aspects, and is intended to be used in life science industry (pharmacy, medicine, cosmetics, etc.). The framework has to propose various tools and activities such as: (1) a platform for searching and analyzing biological information (heterogeneous data, documents, knowledge sources, etc.), (2) knowledge-based models and process for simulation and biology in silico, (3) the management of all activities related to the discovery of new products in collaboration with the industrial laboratories (collaborative work, industrial process management, quality, certification). The “BioIntelligence” project is led by “Dassault Systèmes” and involves industrial partners such as Sanofi Aventis, Laboratoires Pierre Fabre, Ipsen, Servier, Bayer Crops, and two academics, Inserm and Inria. An annual meeting of the project usually takes place in Sophia-Antipolis at the beginning of July.

Two theses related to “BioIntelligence” are currently in preparation within the Orpailleur team. A first thesis is related to the mining of complex biological data using FCA and RCA techniques [47] , [48] , [49] . The objective is to take advantage of Linked Open Data in biology for helping the biologist for querying and navigating complex data. There are needs to integrate data and knowledge from several web biological resources. At present, some experiments are conducted on designing practical interfaces based on sophisticated visualization tools for allowing human agents to have an easy and quick access to interesting patterns .

A second thesis is based on an extension of FCA involving Pattern Structures on complex data such as sequences and graphs [107] . The idea is to extend the formalism of pattern structures to these complex data for being able to classify complex structures such as patient trajectories or molecular structures. The classification results (e.g. concept lattices) are expected to help practitioners in information retrieval tasks and specific problem solving. In addition, a theoretical and practical work was conducted on the evaluation of interest measures for selecting the best concepts to be analyzed by a human agent in a concept lattice, and especially the stability measure in FCA. This led to a series of original and pioneering experiments on this probably underestimated research subject [20] , [54] , [53] .