Section: Research Program

Symbolic methods for model space exploration: Semantic web for life sciences and Formal Concepts Analysis

All the methods presented in the previous sections usually result in pools of candidates which equivalently explain the data and knowledge. These candidates can be dynamical systems, compounds, biological sequences, proteins... In any case, the output of our formal methods generally requires a posteriori investigation and filtering by domain experts. In order to assist them, we rely on two classes of symbolic technics: Semantic Web technologies and Formal Concept Analysis (FCA). They both aim at the formalization and management of knowledge, that is, the explicitation of relations occuring in structured data. These technics complement each other: the production of relevant concepts in FCA highly depends on the availability of semantic annotations using a controlled set of terms and conversely, building and exploiting ontologies is a complex process that can be made much easier with FCA.

Integrating heterogenous data with semantic web technologies The emergence of ontologies in biomedical informatics and bioinformatics happened in parallel with the development of the Semantic Web in the computer science community  [88]. Let us recall that the Semantic Web is an extension of the current Web that provides an infrastructure integrating data and ontologies in order to support unified reasoning. Since the beginning, life sciences have been a major application domain for the Semantic Web  [52]. This was motivated by the joint evolution of data acquisition capabilities in the biomedical field, and of the methods and infrastructures supporting data analysis (grids, the Internet...), resulting in an explosion of data production in complementary domains  [60], [53]. Consequently, Semantic Web technologies have become an integral part of translational medicine and translational bioinformatics  [63]. The Linked Open Data project promotes the integration of data sources in machine-processable formats compatible with the Semantic Web [59], with a strong involvement of life sciences in this initiative.

However, a specificity of life sciences “data deluge” is that the proportion of generated data is much higher than in the more general “big data phenomenon”, and that these data are highly connected  [91]. The bottleneck that once was data scarcity now lies in the lack of adequate methods supporting data integration, processing and analysis. [78]. Each of these steps typically hinges on domain knowledge, which is why they resist automation. This knowledge can be seen as the set of rules representing in what conditions data can be used or can be combined for inferring new data or new links between data.

In this setting, we are working on the integration of Semantic Web resources with our data analysis methods in order to take existing biological knowledge into account. We have introduced several methods to interpret semantic similarities and particularities [58], [57]. We now focus our attention on the semi-automated construction of RDF abstractions of heterogeneous datasets which can be handled by non-expert users. This allows both to automatically prepare input datasets for the other methods developed in the team and to analyse the output of the methods in a wide knowledge context.

Using Formal concept analysis to explore the results of bioinformatics analyses Formal concept analysis aims at the development of conceptual structures which can be logically activated for the formation of judgments and conclusions [96]. It is used in various domains managing structured data such as knowledge processing, information retrieval or classification [79]. In its most simple form, one considers a binary relation between a set of objects and a set of attributes. In this setting, formal concept analysis formalizes the semantic notions of extension and intension. Concepts are related within a lattice structure (Galois connection) by subconcept-superconcept relations, and this allows drawing causality relations between attribute subsets. In bioinformatics, it has been used to derive phylogenetic relations among groups of organisms [77], a classification task that requires to take into account many-valued Galois connections. We have proposed in a similar way a classification scheme for the problem of protein assignment in a set of protein families [65].

One of the most important issue with concept analysis is due to the fact that current methods remain very sensitive to the presence of uncertainty or incompleteness in data. On the other hand, this apparent defect can be reversed to serve as a marker of incompleteness or inconsistency [66]. Following this inspiration, we have proposed a methodology to tackle the problem of uncertainty on biological networks where edges are mostly predicted links with a high level of false positives [97]. The general idea consists to look for a tradeoff between the simplicity of the conceptual representation and the need to manage exceptions. As a very prospective challenge, we are exploring the idea of using ontologies to help this or to help ontology refinement using concept analysis [80], [56], [83].

More generally, common difficult tasks in this context are visualization, search for local structures (graph mining) and network comparison. Network compression is a good solution for an efficient treatment of all these tasks. This has been used with success in power graphs, which are abstract graphs where nodes are clusters of nodes in the initial graph and edges represent bicliques between two sets of nodes [85]. In fact, concepts are maximal bicliques and we are currently developing the power graph idea in the framework of concept analysis.