Section: New Results

KDDK in Life Sciences

Participants : Adrien Coulet, Marie-Dominique Devignes, Bernard Maigret, Gabin Personeni, David Ritchie, Malika Smaïl-Tabbone.

The Life Sciences constitute a challenging domain for KDDK. Biological data are complex from many points of views, e.g. voluminous, high-dimensional and deeply inter-connected. Analyzing such data is a crucial issue in health care, environment and agronomy. Besides, many bio-ontologies are available and can be used to enhance the knowledge discovery process. Accordingly, the research work of the Orpailleur team in KDDK applied to Life Sciences is in concern with the use of bio-ontologies to improve KDDK, and as well information retrieval, access to “Linked Open Data” (LOD) and data integration.

Inductive Logic Programming for Mining Linked Open Data

Increasing amounts of biomedical data provided as LOD offer novel opportunities for knowledge discovery in biomedicine. We proposed and published an approach for selecting, integrating, and mining LOD with the goal of discovering genes responsible for a disease [11] . The selection step relies on a set of choices made by a domain expert to isolate relevant pieces of LOD. Because these pieces are potentially not linked, an integration step is required to connect unlinked pieces. The resulting graph is subsequently mined using Inductive Logic Programming (ILP) that presents two main advantages. First, the input format compliant with ILP (first order logic) is close to the format of LOD (RDF triples). Second, domain knowledge can be added to this input and used during the induction step. We have applied this approach to the characterization of genes responsible for intellectual disability. For this real-world use case, we could evaluate ILP results and assess the contribution of domain knowledge. Our ongoing efforts explore how the combination of rules coming from distinct theories can improve the prediction accuracy [70] [16] .

Analysis of biomedical data annotated with ontologies

Annotating data with concepts of an ontology is a common practice in the biomedical domain. Resulting annotations define links between data and ontologies that are key for data exchange, data integration and data analysis. Since 2011, we collaborate with the National Center for Biomedical Ontologies (NCBO) to develop a large repository of annotations named the NCBO Resource Index  [118] . This repository contains annotations of 36 biomedical databases annotated with concepts of more than 200 ontologies of the BioPortal (http://bioportal.bioontology.org/ ). In the preceding years, we compared the annotations of a database of biomedical publications (Medline) with two databases of scientific funding (Crisp and ResearchCrossroads) to profile disease research  [122] . One main challenge remains to develop a knowledge discovery approach able to mine correlations between annotations based on BioPortal ontologies, i.e. is it possible to discover interesting knowledge units within these annotations?

Then, we proposed an adaptation of FCA techniques, namely pattern structures, to explore the annotations of biomedical databases [108] . We considered documents of biomedical databases annotated with sets of ontological concepts as objects in a pattern structure. Corresponding annotations have been classified according to several dimensions, where a dimension is related to a particular aspect of domain knowledge. The pattern structure formalism was applied to classify these annotations, allowing to discover correlations between annotations but also lacks of completion in the annotations that could be fixed afterward. This adaptation of pattern structures opens many perspectives in term of ontology reengineering and knowledge discovery.